Command that produces this log: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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> trainable params:
>>> pad_vector: torch.Size([1, 1, 1024])
>>> xlmr.embeddings.word_embeddings.weight: torch.Size([250002, 1024])
>>> xlmr.embeddings.position_embeddings.weight: torch.Size([514, 1024])
>>> xlmr.embeddings.token_type_embeddings.weight: torch.Size([1, 1024])
>>> xlmr.embeddings.LayerNorm.weight: torch.Size([1024])
>>> xlmr.embeddings.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.0.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.0.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.0.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.0.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.0.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.1.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.1.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.1.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.1.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.1.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.2.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.2.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.2.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.2.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.2.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.3.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.3.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.3.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.3.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.3.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.4.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.4.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.4.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.4.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.4.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.5.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.5.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.5.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.5.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.5.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.6.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.6.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.6.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.6.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.6.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.7.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.7.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.7.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.7.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.7.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.8.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.8.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.8.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.8.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.8.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.9.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.9.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.9.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.9.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.9.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.10.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.10.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.10.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.10.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.10.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.11.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.11.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.11.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.11.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.11.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.12.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.12.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.12.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.12.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.12.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.13.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.13.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.13.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.13.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.13.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.14.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.14.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.14.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.14.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.14.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.15.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.15.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.15.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.15.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.15.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.16.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.16.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.16.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.16.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.16.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.17.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.17.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.17.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.17.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.17.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.18.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.18.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.18.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.18.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.18.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.19.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.19.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.19.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.19.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.19.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.20.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.20.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.20.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.20.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.20.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.21.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.21.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.21.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.21.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.21.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.22.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.22.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.22.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.22.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.22.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.query.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.query.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.key.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.key.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.self.value.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.self.value.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.dense.weight: torch.Size([1024, 1024])
>>> xlmr.encoder.layer.23.attention.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.attention.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.intermediate.dense.weight: torch.Size([4096, 1024])
>>> xlmr.encoder.layer.23.intermediate.dense.bias: torch.Size([4096])
>>> xlmr.encoder.layer.23.output.dense.weight: torch.Size([1024, 4096])
>>> xlmr.encoder.layer.23.output.dense.bias: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.weight: torch.Size([1024])
>>> xlmr.encoder.layer.23.output.LayerNorm.bias: torch.Size([1024])
>>> xlmr.pooler.dense.weight: torch.Size([1024, 1024])
>>> xlmr.pooler.dense.bias: torch.Size([1024])
>>> upos_embedding.weight: torch.Size([19, 50])
>>> dist_embedding.weight: torch.Size([104, 50])
>>> deprel_embedding.weight: torch.Size([38, 50])
>>> gcn_layer.W.0.weight: torch.Size([450, 50])
>>> gcn_layer.W.0.bias: torch.Size([450])
>>> gcn_layer.W.1.weight: torch.Size([450, 450])
>>> gcn_layer.W.1.bias: torch.Size([450])
>>> trans_rep.weight: torch.Size([1024, 2924])
>>> trans_rep.bias: torch.Size([1024])
>>> mention_label_ffn.layers.0.weight: torch.Size([350, 1024])
>>> mention_label_ffn.layers.0.bias: torch.Size([350])
>>> mention_label_ffn.layers.1.weight: torch.Size([11, 350])
>>> mention_label_ffn.layers.1.bias: torch.Size([11])
>>> trigger_label_ffn.layers.0.weight: torch.Size([450, 1024])
>>> trigger_label_ffn.layers.0.bias: torch.Size([450])
>>> trigger_label_ffn.layers.1.weight: torch.Size([233, 450])
>>> trigger_label_ffn.layers.1.bias: torch.Size([233])
>>> event_type_ffn.layers.0.weight: torch.Size([450, 1024])
>>> event_type_ffn.layers.0.bias: torch.Size([450])
>>> event_type_ffn.layers.1.weight: torch.Size([117, 450])
>>> event_type_ffn.layers.1.bias: torch.Size([117])
>>> role_type_ffn.layers.0.weight: torch.Size([350, 1024])
>>> role_type_ffn.layers.0.bias: torch.Size([350])
>>> role_type_ffn.layers.1.weight: torch.Size([6, 350])
>>> role_type_ffn.layers.1.bias: torch.Size([6])
>>> trigger_crf.transition: torch.Size([235, 235])
>>> mention_crf.transition: torch.Size([13, 13])
n_trainable_params: 564979817, n_nontrainable_params: 0
----------------------------------------------------------------------------------------------------
******************************
Epoch: 0
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:26:22.153081: step: 3074/1536, loss: 1.4075449705123901
==================================================
Loss: 4.309
--------------------
Dev Chinese: {'event': {'p': 0.5764828303850156, 'r': 0.7376830892143809, 'f1': 0.647196261682243}, 'argument': {'p': 0.5627706886601762, 'r': 0.5094294551280948, 'f1': 0.5347732267098627}, 'combined': 0.34610323317437375, 'epoch': 0}
Test Chinese: {'event': {'p': 0.6356011183597391, 'r': 0.7843588269120184, 'f1': 0.7021879021879022}, 'argument': {'p': 0.5892703075327577, 'r': 0.5229673214936751, 'f1': 0.5541425794700149}, 'combined': 0.3891122153910426, 'epoch': 0}
Dev Korean: {'event': {'p': 0.5467980295566502, 'r': 0.7390146471371505, 'f1': 0.6285390713476784}, 'argument': {'p': 0.564959922003289, 'r': 0.49275008447781415, 'f1': 0.5263901212769202}, 'combined': 0.3308567579939872, 'epoch': 0}
Test Korean: {'event': {'p': 0.589585172109444, 'r': 0.7682576193214491, 'f1': 0.6671660424469413}, 'argument': {'p': 0.5970838116770081, 'r': 0.5238680317804727, 'f1': 0.5580848509360274}, 'combined': 0.37233526134858064, 'epoch': 0}
Dev Russian: {'event': {'p': 0.6049250535331906, 'r': 0.7523302263648469, 'f1': 0.6706231454005935}, 'argument': {'p': 0.5662002128385617, 'r': 0.5006139711754438, 'f1': 0.5313910168741677}, 'combined': 0.3563631151737742, 'epoch': 0}
Test Russian: {'event': {'p': 0.6444121915820029, 'r': 0.7659574468085106, 'f1': 0.6999474513925381}, 'argument': {'p': 0.5906987488000502, 'r': 0.5201095321959569, 'f1': 0.5531612523299789}, 'combined': 0.38718380877747344, 'epoch': 0}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6338501634420002, 'r': 0.5133662480769918, 'f1': 0.5672814248156713}, 'combined': 0.3999538103016963, 'epoch': 0}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.3932220160791589, 'r': 0.17873728003598133, 'f1': 0.2457637600494743}, 'combined': 0.15123923695352262, 'epoch': 0}
Russian: {'event': {'p': 0.4791666666666667, 'r': 0.6388888888888888, 'f1': 0.5476190476190476}, 'argument': {'p': 0.36929535289797644, 'r': 0.3155796652037253, 'f1': 0.3403310114942135}, 'combined': 0.18637174438968834, 'epoch': 0}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5764828303850156, 'r': 0.7376830892143809, 'f1': 0.647196261682243}, 'argument': {'p': 0.5627706886601762, 'r': 0.5094294551280948, 'f1': 0.5347732267098627}, 'combined': 0.34610323317437375, 'epoch': 0}
Test for Chinese: {'event': {'p': 0.6356011183597391, 'r': 0.7843588269120184, 'f1': 0.7021879021879022}, 'argument': {'p': 0.5892703075327577, 'r': 0.5229673214936751, 'f1': 0.5541425794700149}, 'combined': 0.3891122153910426, 'epoch': 0}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6338501634420002, 'r': 0.5133662480769918, 'f1': 0.5672814248156713}, 'combined': 0.3999538103016963, 'epoch': 0}
--------------------
Dev for Korean: {'event': {'p': 0.5467980295566502, 'r': 0.7390146471371505, 'f1': 0.6285390713476784}, 'argument': {'p': 0.564959922003289, 'r': 0.49275008447781415, 'f1': 0.5263901212769202}, 'combined': 0.3308567579939872, 'epoch': 0}
Test for Korean: {'event': {'p': 0.589585172109444, 'r': 0.7682576193214491, 'f1': 0.6671660424469413}, 'argument': {'p': 0.5970838116770081, 'r': 0.5238680317804727, 'f1': 0.5580848509360274}, 'combined': 0.37233526134858064, 'epoch': 0}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.3932220160791589, 'r': 0.17873728003598133, 'f1': 0.2457637600494743}, 'combined': 0.15123923695352262, 'epoch': 0}
--------------------
Dev for Russian: {'event': {'p': 0.6049250535331906, 'r': 0.7523302263648469, 'f1': 0.6706231454005935}, 'argument': {'p': 0.5662002128385617, 'r': 0.5006139711754438, 'f1': 0.5313910168741677}, 'combined': 0.3563631151737742, 'epoch': 0}
Test for Russian: {'event': {'p': 0.6444121915820029, 'r': 0.7659574468085106, 'f1': 0.6999474513925381}, 'argument': {'p': 0.5906987488000502, 'r': 0.5201095321959569, 'f1': 0.5531612523299789}, 'combined': 0.38718380877747344, 'epoch': 0}
Russian: {'event': {'p': 0.4791666666666667, 'r': 0.6388888888888888, 'f1': 0.5476190476190476}, 'argument': {'p': 0.36929535289797644, 'r': 0.3155796652037253, 'f1': 0.3403310114942135}, 'combined': 0.18637174438968834, 'epoch': 0}
******************************
Epoch: 1
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 14:55:19.562269: step: 3074/1536, loss: 0.39199745655059814
==================================================
Loss: 1.702
--------------------
Dev Chinese: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6227011227721931, 'r': 0.6569656746488729, 'f1': 0.6393746622966182}, 'combined': 0.4197763553863311, 'epoch': 1}
Test Chinese: {'event': {'p': 0.6472506989748369, 'r': 0.7987349051178838, 'f1': 0.7150579150579152}, 'argument': {'p': 0.6224487015286491, 'r': 0.671539056616774, 'f1': 0.6460627021942951}, 'combined': 0.46197224882773535, 'epoch': 1}
Dev Korean: {'event': {'p': 0.5517241379310345, 'r': 0.7456724367509987, 'f1': 0.6342015855039639}, 'argument': {'p': 0.626638258942128, 'r': 0.6366426433374305, 'f1': 0.6316008369467092}, 'combined': 0.40056225219723357, 'epoch': 1}
Test Korean: {'event': {'p': 0.5979699911738746, 'r': 0.7791834387579069, 'f1': 0.6766541822721597}, 'argument': {'p': 0.62414628770282, 'r': 0.6787882090558325, 'f1': 0.6503214733704611}, 'combined': 0.4400427447775154, 'epoch': 1}
Dev Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6274270530254458, 'r': 0.6522847645906505, 'f1': 0.6396144849906882}, 'combined': 0.4319770230975687, 'epoch': 1}
Test Russian: {'event': {'p': 0.6555394291243348, 'r': 0.7791834387579069, 'f1': 0.7120336311087757}, 'argument': {'p': 0.6213534394592369, 'r': 0.6701249577017151, 'f1': 0.6448182923551355}, 'combined': 0.45913231011098726, 'epoch': 1}
Chinese: {'event': {'p': 0.5647058823529412, 'r': 0.8888888888888888, 'f1': 0.6906474820143885}, 'argument': {'p': 0.603579098067287, 'r': 0.6335086401202104, 'f1': 0.6181818181818182}, 'combined': 0.4269457161543493, 'epoch': 1}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.5354754585189367, 'r': 0.31705783728094944, 'f1': 0.39828753112978776}, 'combined': 0.25122751963571227, 'epoch': 1}
Russian: {'event': {'p': 0.4166666666666667, 'r': 0.5555555555555556, 'f1': 0.4761904761904762}, 'argument': {'p': 0.46694261070415627, 'r': 0.5433514015466546, 'f1': 0.5022575980683361}, 'combined': 0.2391702847944458, 'epoch': 1}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6227011227721931, 'r': 0.6569656746488729, 'f1': 0.6393746622966182}, 'combined': 0.4197763553863311, 'epoch': 1}
Test for Chinese: {'event': {'p': 0.6472506989748369, 'r': 0.7987349051178838, 'f1': 0.7150579150579152}, 'argument': {'p': 0.6224487015286491, 'r': 0.671539056616774, 'f1': 0.6460627021942951}, 'combined': 0.46197224882773535, 'epoch': 1}
Chinese: {'event': {'p': 0.5647058823529412, 'r': 0.8888888888888888, 'f1': 0.6906474820143885}, 'argument': {'p': 0.603579098067287, 'r': 0.6335086401202104, 'f1': 0.6181818181818182}, 'combined': 0.4269457161543493, 'epoch': 1}
--------------------
Dev for Korean: {'event': {'p': 0.5517241379310345, 'r': 0.7456724367509987, 'f1': 0.6342015855039639}, 'argument': {'p': 0.626638258942128, 'r': 0.6366426433374305, 'f1': 0.6316008369467092}, 'combined': 0.40056225219723357, 'epoch': 1}
Test for Korean: {'event': {'p': 0.5979699911738746, 'r': 0.7791834387579069, 'f1': 0.6766541822721597}, 'argument': {'p': 0.62414628770282, 'r': 0.6787882090558325, 'f1': 0.6503214733704611}, 'combined': 0.4400427447775154, 'epoch': 1}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.5354754585189367, 'r': 0.31705783728094944, 'f1': 0.39828753112978776}, 'combined': 0.25122751963571227, 'epoch': 1}
--------------------
Dev for Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6274270530254458, 'r': 0.6522847645906505, 'f1': 0.6396144849906882}, 'combined': 0.4319770230975687, 'epoch': 1}
Test for Russian: {'event': {'p': 0.6555394291243348, 'r': 0.7791834387579069, 'f1': 0.7120336311087757}, 'argument': {'p': 0.6213534394592369, 'r': 0.6701249577017151, 'f1': 0.6448182923551355}, 'combined': 0.45913231011098726, 'epoch': 1}
Russian: {'event': {'p': 0.4166666666666667, 'r': 0.5555555555555556, 'f1': 0.4761904761904762}, 'argument': {'p': 0.46694261070415627, 'r': 0.5433514015466546, 'f1': 0.5022575980683361}, 'combined': 0.2391702847944458, 'epoch': 1}
******************************
Epoch: 2
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:24:22.605272: step: 3074/1536, loss: 3.497777223587036
==================================================
Loss: 1.449
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6642317243349262, 'r': 0.6447241847068813, 'f1': 0.6543325928247187}, 'combined': 0.4334189020229154, 'epoch': 2}
Test Chinese: {'event': {'p': 0.646784715750233, 'r': 0.7981598619896493, 'f1': 0.7145431145431146}, 'argument': {'p': 0.6825772561070389, 'r': 0.6669421531466383, 'f1': 0.6746691329600267}, 'combined': 0.4820801835513602, 'epoch': 2}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6674154680706477, 'r': 0.6301215703627813, 'f1': 0.6482325673459592}, 'combined': 0.4162489985109388, 'epoch': 2}
Test Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6807179723499447, 'r': 0.6696230784033242, 'f1': 0.6751249455315953}, 'combined': 0.4601975284147953, 'epoch': 2}
Dev Russian: {'event': {'p': 0.619914346895075, 'r': 0.7709720372836218, 'f1': 0.687240356083086}, 'argument': {'p': 0.663892461827417, 'r': 0.6439367499982939, 'f1': 0.65376235727404}, 'combined': 0.4492918752067289, 'epoch': 2}
Test Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6798221742412407, 'r': 0.66195068159429, 'f1': 0.67076740991556}, 'combined': 0.47937134917769914, 'epoch': 2}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6394121394121394, 'r': 0.5865681609483262, 'f1': 0.6118512713340298}, 'combined': 0.4313771553290282, 'epoch': 2}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5934193121693121, 'r': 0.36055856941932884, 'f1': 0.44856892888388944}, 'combined': 0.2760424177747012, 'epoch': 2}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.537326091099676, 'r': 0.5177869605142332, 'f1': 0.5273756079311636}, 'combined': 0.2636878039655818, 'epoch': 2}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6642317243349262, 'r': 0.6447241847068813, 'f1': 0.6543325928247187}, 'combined': 0.4334189020229154, 'epoch': 2}
Test for Chinese: {'event': {'p': 0.646784715750233, 'r': 0.7981598619896493, 'f1': 0.7145431145431146}, 'argument': {'p': 0.6825772561070389, 'r': 0.6669421531466383, 'f1': 0.6746691329600267}, 'combined': 0.4820801835513602, 'epoch': 2}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6394121394121394, 'r': 0.5865681609483262, 'f1': 0.6118512713340298}, 'combined': 0.4313771553290282, 'epoch': 2}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6674154680706477, 'r': 0.6301215703627813, 'f1': 0.6482325673459592}, 'combined': 0.4162489985109388, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6807179723499447, 'r': 0.6696230784033242, 'f1': 0.6751249455315953}, 'combined': 0.4601975284147953, 'epoch': 2}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5934193121693121, 'r': 0.36055856941932884, 'f1': 0.44856892888388944}, 'combined': 0.2760424177747012, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.619914346895075, 'r': 0.7709720372836218, 'f1': 0.687240356083086}, 'argument': {'p': 0.663892461827417, 'r': 0.6439367499982939, 'f1': 0.65376235727404}, 'combined': 0.4492918752067289, 'epoch': 2}
Test for Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6798221742412407, 'r': 0.66195068159429, 'f1': 0.67076740991556}, 'combined': 0.47937134917769914, 'epoch': 2}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.537326091099676, 'r': 0.5177869605142332, 'f1': 0.5273756079311636}, 'combined': 0.2636878039655818, 'epoch': 2}
******************************
Epoch: 3
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 15:53:18.659819: step: 3074/1536, loss: 0.28219908475875854
==================================================
Loss: 1.069
--------------------
Dev Chinese: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.66556923054199, 'r': 0.6558029909155485, 'f1': 0.6606500196732392}, 'combined': 0.4337445222621033, 'epoch': 3}
Test Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.6868089430228964, 'r': 0.6825896389360663, 'f1': 0.6846927908533633}, 'combined': 0.49065244008642567, 'epoch': 3}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6704007961301842, 'r': 0.6392646198222511, 'f1': 0.6544625899814724}, 'combined': 0.4202494773720214, 'epoch': 3}
Test Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.6905521727347119, 'r': 0.6837521683172904, 'f1': 0.6871353474473367}, 'combined': 0.46769811663956057, 'epoch': 3}
Dev Russian: {'event': {'p': 0.6102783725910065, 'r': 0.758988015978695, 'f1': 0.6765578635014837}, 'argument': {'p': 0.6672289948856985, 'r': 0.6501205591193986, 'f1': 0.6585636832638064}, 'combined': 0.4455564385286287, 'epoch': 3}
Test Russian: {'event': {'p': 0.6584421867440735, 'r': 0.7826336975273146, 'f1': 0.7151865475564897}, 'argument': {'p': 0.6871203754037913, 'r': 0.6785788237758437, 'f1': 0.6828228886917453}, 'combined': 0.4883457443559986, 'epoch': 3}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6333333333333333, 'r': 0.628099173553719, 'f1': 0.6307053941908712}, 'combined': 0.4446699901489595, 'epoch': 3}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.46114864864864863, 'r': 0.22450657894736842, 'f1': 0.3019911504424779}, 'combined': 0.1904867256637168, 'epoch': 3}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5217958236560777, 'r': 0.5502574140373183, 'f1': 0.5356488101248231}, 'combined': 0.28057794816062165, 'epoch': 3}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.66556923054199, 'r': 0.6558029909155485, 'f1': 0.6606500196732392}, 'combined': 0.4337445222621033, 'epoch': 3}
Test for Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.6868089430228964, 'r': 0.6825896389360663, 'f1': 0.6846927908533633}, 'combined': 0.49065244008642567, 'epoch': 3}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6333333333333333, 'r': 0.628099173553719, 'f1': 0.6307053941908712}, 'combined': 0.4446699901489595, 'epoch': 3}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6674154680706477, 'r': 0.6301215703627813, 'f1': 0.6482325673459592}, 'combined': 0.4162489985109388, 'epoch': 2}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6807179723499447, 'r': 0.6696230784033242, 'f1': 0.6751249455315953}, 'combined': 0.4601975284147953, 'epoch': 2}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5934193121693121, 'r': 0.36055856941932884, 'f1': 0.44856892888388944}, 'combined': 0.2760424177747012, 'epoch': 2}
--------------------
Dev for Russian: {'event': {'p': 0.6102783725910065, 'r': 0.758988015978695, 'f1': 0.6765578635014837}, 'argument': {'p': 0.6672289948856985, 'r': 0.6501205591193986, 'f1': 0.6585636832638064}, 'combined': 0.4455564385286287, 'epoch': 3}
Test for Russian: {'event': {'p': 0.6584421867440735, 'r': 0.7826336975273146, 'f1': 0.7151865475564897}, 'argument': {'p': 0.6871203754037913, 'r': 0.6785788237758437, 'f1': 0.6828228886917453}, 'combined': 0.4883457443559986, 'epoch': 3}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5217958236560777, 'r': 0.5502574140373183, 'f1': 0.5356488101248231}, 'combined': 0.28057794816062165, 'epoch': 3}
******************************
Epoch: 4
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 15:59:30.405212: step: 2/1536, loss: 0.44445469975471497
2023-01-21 15:59:31.323180: step: 4/1536, loss: 1.2928177118301392
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2023-01-21 16:22:19.160174: step: 3072/1536, loss: 7.0123419761657715
2023-01-21 16:22:20.023814: step: 3074/1536, loss: 0.8479297161102295
==================================================
Loss: 0.815
--------------------
Dev Chinese: {'event': {'p': 0.5796045785639958, 'r': 0.7416777629826897, 'f1': 0.6507009345794392}, 'argument': {'p': 0.6138299744366941, 'r': 0.7036477427550044, 'f1': 0.6556772388809907}, 'combined': 0.4266497921223269, 'epoch': 4}
Test Chinese: {'event': {'p': 0.6430568499534017, 'r': 0.7935595169637722, 'f1': 0.7104247104247106}, 'argument': {'p': 0.6296561337136084, 'r': 0.7207736083533933, 'f1': 0.6721408887573018}, 'combined': 0.47750549626001376, 'epoch': 4}
Dev Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6167963913268004, 'r': 0.6824986156203074, 'f1': 0.6479863017241309}, 'combined': 0.417558556830159, 'epoch': 4}
Test Korean: {'event': {'p': 0.5975286849073257, 'r': 0.7786083956296722, 'f1': 0.6761548064918851}, 'argument': {'p': 0.6269851955813445, 'r': 0.7175875338844832, 'f1': 0.6692338025597774}, 'combined': 0.45250565226763473, 'epoch': 4}
Dev Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6169548888570319, 'r': 0.6994258491644364, 'f1': 0.655606975355093}, 'combined': 0.4427778860261697, 'epoch': 4}
Test Russian: {'event': {'p': 0.6545718432510885, 'r': 0.7780333525014376, 'f1': 0.7109826589595376}, 'argument': {'p': 0.6267112605649101, 'r': 0.713947384784818, 'f1': 0.6674911127415196}, 'combined': 0.47457460616882613, 'epoch': 4}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.5790640394088671, 'r': 0.6939197166469895, 'f1': 0.631310418904404}, 'combined': 0.4541801574851827, 'epoch': 4}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6043792517006803, 'r': 0.4284207353827607, 'f1': 0.501410934744268}, 'combined': 0.30856057522724184, 'epoch': 4}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5409419595249554, 'r': 0.6392950430749473, 'f1': 0.586020456152035}, 'combined': 0.3069630960796374, 'epoch': 4}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5796045785639958, 'r': 0.7416777629826897, 'f1': 0.6507009345794392}, 'argument': {'p': 0.6138299744366941, 'r': 0.7036477427550044, 'f1': 0.6556772388809907}, 'combined': 0.4266497921223269, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6430568499534017, 'r': 0.7935595169637722, 'f1': 0.7104247104247106}, 'argument': {'p': 0.6296561337136084, 'r': 0.7207736083533933, 'f1': 0.6721408887573018}, 'combined': 0.47750549626001376, 'epoch': 4}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.5790640394088671, 'r': 0.6939197166469895, 'f1': 0.631310418904404}, 'combined': 0.4541801574851827, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6167963913268004, 'r': 0.6824986156203074, 'f1': 0.6479863017241309}, 'combined': 0.417558556830159, 'epoch': 4}
Test for Korean: {'event': {'p': 0.5975286849073257, 'r': 0.7786083956296722, 'f1': 0.6761548064918851}, 'argument': {'p': 0.6269851955813445, 'r': 0.7175875338844832, 'f1': 0.6692338025597774}, 'combined': 0.45250565226763473, 'epoch': 4}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6043792517006803, 'r': 0.4284207353827607, 'f1': 0.501410934744268}, 'combined': 0.30856057522724184, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6169548888570319, 'r': 0.6994258491644364, 'f1': 0.655606975355093}, 'combined': 0.4427778860261697, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6545718432510885, 'r': 0.7780333525014376, 'f1': 0.7109826589595376}, 'argument': {'p': 0.6267112605649101, 'r': 0.713947384784818, 'f1': 0.6674911127415196}, 'combined': 0.47457460616882613, 'epoch': 4}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5409419595249554, 'r': 0.6392950430749473, 'f1': 0.586020456152035}, 'combined': 0.3069630960796374, 'epoch': 4}
******************************
Epoch: 5
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 16:28:42.264041: step: 2/1536, loss: 0.9316489100456238
2023-01-21 16:28:43.156123: step: 4/1536, loss: 1.4836921691894531
2023-01-21 16:28:44.081473: step: 6/1536, loss: 0.5419933199882507
2023-01-21 16:28:45.084739: step: 8/1536, loss: 0.4896971881389618
2023-01-21 16:28:45.960978: step: 10/1536, loss: 0.4110070765018463
2023-01-21 16:28:46.833472: step: 12/1536, loss: 0.5126852989196777
2023-01-21 16:28:47.722546: step: 14/1536, loss: 0.5098076462745667
2023-01-21 16:28:48.626882: step: 16/1536, loss: 1.1721340417861938
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2023-01-21 16:51:31.751709: step: 3074/1536, loss: 1.6719058752059937
==================================================
Loss: 0.721
--------------------
Dev Chinese: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6450772365187518, 'r': 0.6649694861030305, 'f1': 0.6548723355548284}, 'combined': 0.4330113807523748, 'epoch': 5}
Test Chinese: {'event': {'p': 0.646784715750233, 'r': 0.7981598619896493, 'f1': 0.7145431145431146}, 'argument': {'p': 0.6666881046618874, 'r': 0.6853526401969762, 'f1': 0.6758915431437519}, 'combined': 0.4829536483312884, 'epoch': 5}
Dev Korean: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6513350879359366, 'r': 0.650389067474156, 'f1': 0.6508617339476112}, 'combined': 0.41646305739569683, 'epoch': 5}
Test Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.6673015711084725, 'r': 0.6852020207239461, 'f1': 0.6761333392635352}, 'combined': 0.46020960869722777, 'epoch': 5}
Dev Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6436108301829921, 'r': 0.6629569035242697, 'f1': 0.6531406402362614}, 'combined': 0.4473141239362883, 'epoch': 5}
Test Russian: {'event': {'p': 0.6608611514271892, 'r': 0.7855089131684876, 'f1': 0.7178139779295848}, 'argument': {'p': 0.6635185932106645, 'r': 0.6798026651491219, 'f1': 0.6715619294097352}, 'combined': 0.48205653997566905, 'epoch': 5}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.615090909090909, 'r': 0.6354244928625093, 'f1': 0.6250923872875093}, 'combined': 0.44071261837536624, 'epoch': 5}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.594957729468599, 'r': 0.3601059941520468, 'f1': 0.4486566484517304}, 'combined': 0.2829988090233992, 'epoch': 5}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5029795474736386, 'r': 0.5774950359882517, 'f1': 0.5376677921269929}, 'combined': 0.2816355101617582, 'epoch': 5}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5796045785639958, 'r': 0.7416777629826897, 'f1': 0.6507009345794392}, 'argument': {'p': 0.6138299744366941, 'r': 0.7036477427550044, 'f1': 0.6556772388809907}, 'combined': 0.4266497921223269, 'epoch': 4}
Test for Chinese: {'event': {'p': 0.6430568499534017, 'r': 0.7935595169637722, 'f1': 0.7104247104247106}, 'argument': {'p': 0.6296561337136084, 'r': 0.7207736083533933, 'f1': 0.6721408887573018}, 'combined': 0.47750549626001376, 'epoch': 4}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.5790640394088671, 'r': 0.6939197166469895, 'f1': 0.631310418904404}, 'combined': 0.4541801574851827, 'epoch': 4}
--------------------
Dev for Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6167963913268004, 'r': 0.6824986156203074, 'f1': 0.6479863017241309}, 'combined': 0.417558556830159, 'epoch': 4}
Test for Korean: {'event': {'p': 0.5975286849073257, 'r': 0.7786083956296722, 'f1': 0.6761548064918851}, 'argument': {'p': 0.6269851955813445, 'r': 0.7175875338844832, 'f1': 0.6692338025597774}, 'combined': 0.45250565226763473, 'epoch': 4}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6043792517006803, 'r': 0.4284207353827607, 'f1': 0.501410934744268}, 'combined': 0.30856057522724184, 'epoch': 4}
--------------------
Dev for Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6169548888570319, 'r': 0.6994258491644364, 'f1': 0.655606975355093}, 'combined': 0.4427778860261697, 'epoch': 4}
Test for Russian: {'event': {'p': 0.6545718432510885, 'r': 0.7780333525014376, 'f1': 0.7109826589595376}, 'argument': {'p': 0.6267112605649101, 'r': 0.713947384784818, 'f1': 0.6674911127415196}, 'combined': 0.47457460616882613, 'epoch': 4}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5409419595249554, 'r': 0.6392950430749473, 'f1': 0.586020456152035}, 'combined': 0.3069630960796374, 'epoch': 4}
******************************
Epoch: 6
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.584
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6681141926130586, 'r': 0.6710574269417504, 'f1': 0.6695825754539664}, 'combined': 0.4435202339747652, 'epoch': 6}
Test Chinese: {'event': {'p': 0.6458527493010252, 'r': 0.79700977573318, 'f1': 0.7135135135135134}, 'argument': {'p': 0.6779181003093216, 'r': 0.6864846883255256, 'f1': 0.6821745010748815}, 'combined': 0.48674072509126676, 'epoch': 6}
Dev Korean: {'event': {'p': 0.5625615763546798, 'r': 0.7603195739014648, 'f1': 0.6466591166477916}, 'argument': {'p': 0.6705071942201172, 'r': 0.6583603247596078, 'f1': 0.6643782436330959}, 'combined': 0.4296262481477891, 'epoch': 6}
Test Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.6767008391804658, 'r': 0.6875004790587279, 'f1': 0.6820579116819786}, 'combined': 0.4642421641061358, 'epoch': 6}
Dev Russian: {'event': {'p': 0.6209850107066381, 'r': 0.7723035952063915, 'f1': 0.6884272997032641}, 'argument': {'p': 0.667533416412618, 'r': 0.6665553454728192, 'f1': 0.6670440224123154}, 'combined': 0.4592113151325139, 'epoch': 6}
Test Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6771141004481458, 'r': 0.6853172826965657, 'f1': 0.6811909959437049}, 'combined': 0.485746811085448, 'epoch': 6}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6633816036470904, 'r': 0.6195216629100927, 'f1': 0.6407018907018908}, 'combined': 0.4609366120157487, 'epoch': 6}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6246498599439776, 'r': 0.4084249084249084, 'f1': 0.4939091915836102}, 'combined': 0.3115427208450464, 'epoch': 6}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5721736529875336, 'r': 0.6033831249686719, 'f1': 0.5873641039518045}, 'combined': 0.3076669115938024, 'epoch': 6}
New best chinese model...
New best korean model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6681141926130586, 'r': 0.6710574269417504, 'f1': 0.6695825754539664}, 'combined': 0.4435202339747652, 'epoch': 6}
Test for Chinese: {'event': {'p': 0.6458527493010252, 'r': 0.79700977573318, 'f1': 0.7135135135135134}, 'argument': {'p': 0.6779181003093216, 'r': 0.6864846883255256, 'f1': 0.6821745010748815}, 'combined': 0.48674072509126676, 'epoch': 6}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6633816036470904, 'r': 0.6195216629100927, 'f1': 0.6407018907018908}, 'combined': 0.4609366120157487, 'epoch': 6}
--------------------
Dev for Korean: {'event': {'p': 0.5625615763546798, 'r': 0.7603195739014648, 'f1': 0.6466591166477916}, 'argument': {'p': 0.6705071942201172, 'r': 0.6583603247596078, 'f1': 0.6643782436330959}, 'combined': 0.4296262481477891, 'epoch': 6}
Test for Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.6767008391804658, 'r': 0.6875004790587279, 'f1': 0.6820579116819786}, 'combined': 0.4642421641061358, 'epoch': 6}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6246498599439776, 'r': 0.4084249084249084, 'f1': 0.4939091915836102}, 'combined': 0.3115427208450464, 'epoch': 6}
--------------------
Dev for Russian: {'event': {'p': 0.6209850107066381, 'r': 0.7723035952063915, 'f1': 0.6884272997032641}, 'argument': {'p': 0.667533416412618, 'r': 0.6665553454728192, 'f1': 0.6670440224123154}, 'combined': 0.4592113151325139, 'epoch': 6}
Test for Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6771141004481458, 'r': 0.6853172826965657, 'f1': 0.6811909959437049}, 'combined': 0.485746811085448, 'epoch': 6}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5721736529875336, 'r': 0.6033831249686719, 'f1': 0.5873641039518045}, 'combined': 0.3076669115938024, 'epoch': 6}
******************************
Epoch: 7
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 17:49:16.771713: step: 3074/1536, loss: 0.3445248603820801
==================================================
Loss: 0.475
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6688731315756482, 'r': 0.6492292804280521, 'f1': 0.6589048285715402}, 'combined': 0.43413822817096803, 'epoch': 7}
Test Chinese: {'event': {'p': 0.6444547996272134, 'r': 0.7952846463484762, 'f1': 0.711969111969112}, 'argument': {'p': 0.6750973172286543, 'r': 0.6785558075833092, 'f1': 0.6768221443038426}, 'combined': 0.48187646104103693, 'epoch': 7}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6771052432339354, 'r': 0.6314742377116485, 'f1': 0.6534941492628983}, 'combined': 0.4196276133998452, 'epoch': 7}
Test Korean: {'event': {'p': 0.6028243601059136, 'r': 0.7855089131684876, 'f1': 0.6821473158551811}, 'argument': {'p': 0.6708910639130189, 'r': 0.675901112591256, 'f1': 0.6733867695954818}, 'combined': 0.4593489774119492, 'epoch': 7}
Dev Russian: {'event': {'p': 0.6124197002141327, 'r': 0.7616511318242344, 'f1': 0.6789317507418398}, 'argument': {'p': 0.6753839684094493, 'r': 0.6452019742167925, 'f1': 0.6599480665462133}, 'combined': 0.4480596962189128, 'epoch': 7}
Test Russian: {'event': {'p': 0.6594097726173198, 'r': 0.7837837837837838, 'f1': 0.7162375197057278}, 'argument': {'p': 0.6692347089882266, 'r': 0.67548491208151, 'f1': 0.6723452851876899}, 'combined': 0.4815589194486712, 'epoch': 7}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7101239669421487, 'r': 0.6455672426746807, 'f1': 0.6763085399449035}, 'combined': 0.48655290643518234, 'epoch': 7}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6559899322047661, 'r': 0.44020377029530355, 'f1': 0.526858055786505}, 'combined': 0.3242203420224646, 'epoch': 7}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5596384328671777, 'r': 0.5291127001653317, 'f1': 0.5439476356839859}, 'combined': 0.284924952024945, 'epoch': 7}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6688731315756482, 'r': 0.6492292804280521, 'f1': 0.6589048285715402}, 'combined': 0.43413822817096803, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6444547996272134, 'r': 0.7952846463484762, 'f1': 0.711969111969112}, 'argument': {'p': 0.6750973172286543, 'r': 0.6785558075833092, 'f1': 0.6768221443038426}, 'combined': 0.48187646104103693, 'epoch': 7}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7101239669421487, 'r': 0.6455672426746807, 'f1': 0.6763085399449035}, 'combined': 0.48655290643518234, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6771052432339354, 'r': 0.6314742377116485, 'f1': 0.6534941492628983}, 'combined': 0.4196276133998452, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6028243601059136, 'r': 0.7855089131684876, 'f1': 0.6821473158551811}, 'argument': {'p': 0.6708910639130189, 'r': 0.675901112591256, 'f1': 0.6733867695954818}, 'combined': 0.4593489774119492, 'epoch': 7}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6559899322047661, 'r': 0.44020377029530355, 'f1': 0.526858055786505}, 'combined': 0.3242203420224646, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6209850107066381, 'r': 0.7723035952063915, 'f1': 0.6884272997032641}, 'argument': {'p': 0.667533416412618, 'r': 0.6665553454728192, 'f1': 0.6670440224123154}, 'combined': 0.4592113151325139, 'epoch': 6}
Test for Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6771141004481458, 'r': 0.6853172826965657, 'f1': 0.6811909959437049}, 'combined': 0.485746811085448, 'epoch': 6}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5721736529875336, 'r': 0.6033831249686719, 'f1': 0.5873641039518045}, 'combined': 0.3076669115938024, 'epoch': 6}
******************************
Epoch: 8
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 18:18:10.942082: step: 3074/1536, loss: 0.2946949303150177
==================================================
Loss: 0.389
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6737997930307842, 'r': 0.6777545973919333, 'f1': 0.6757714091040272}, 'combined': 0.4452512555311581, 'epoch': 8}
Test Chinese: {'event': {'p': 0.6495806150978565, 'r': 0.8016101207590569, 'f1': 0.7176319176319177}, 'argument': {'p': 0.6775965645696277, 'r': 0.694941186529601, 'f1': 0.6861592844251833}, 'combined': 0.4924098030829887, 'epoch': 8}
Dev Korean: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.678357561447443, 'r': 0.6626161241984813, 'f1': 0.67039444994109}, 'combined': 0.42744289390354884, 'epoch': 8}
Test Korean: {'event': {'p': 0.6006178287731686, 'r': 0.7826336975273146, 'f1': 0.6796504369538078}, 'argument': {'p': 0.6769342153025035, 'r': 0.6944094192301496, 'f1': 0.6855604729086388}, 'combined': 0.46594147497061544, 'epoch': 8}
Dev Russian: {'event': {'p': 0.6081370449678801, 'r': 0.7563249001331558, 'f1': 0.6741839762611276}, 'argument': {'p': 0.674797441105508, 'r': 0.679741012102618, 'f1': 0.6772602054891047}, 'combined': 0.456597978300073, 'epoch': 8}
Test Russian: {'event': {'p': 0.660377358490566, 'r': 0.7849338700402531, 'f1': 0.717288491854966}, 'argument': {'p': 0.6774797405866069, 'r': 0.6896739069107687, 'f1': 0.6835224415318047}, 'combined': 0.4902827812353724, 'epoch': 8}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6590909090909091, 'r': 0.6590909090909091, 'f1': 0.6590909090909091}, 'combined': 0.46468279921517325, 'epoch': 8}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.6258503401360545, 'r': 0.39316239316239315, 'f1': 0.4829396325459318}, 'combined': 0.28976377952755905, 'epoch': 8}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.5714821447108895, 'r': 0.5403103913630228, 'f1': 0.5554592808404908}, 'combined': 0.2777296404202454, 'epoch': 8}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6688731315756482, 'r': 0.6492292804280521, 'f1': 0.6589048285715402}, 'combined': 0.43413822817096803, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6444547996272134, 'r': 0.7952846463484762, 'f1': 0.711969111969112}, 'argument': {'p': 0.6750973172286543, 'r': 0.6785558075833092, 'f1': 0.6768221443038426}, 'combined': 0.48187646104103693, 'epoch': 7}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7101239669421487, 'r': 0.6455672426746807, 'f1': 0.6763085399449035}, 'combined': 0.48655290643518234, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6771052432339354, 'r': 0.6314742377116485, 'f1': 0.6534941492628983}, 'combined': 0.4196276133998452, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6028243601059136, 'r': 0.7855089131684876, 'f1': 0.6821473158551811}, 'argument': {'p': 0.6708910639130189, 'r': 0.675901112591256, 'f1': 0.6733867695954818}, 'combined': 0.4593489774119492, 'epoch': 7}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6559899322047661, 'r': 0.44020377029530355, 'f1': 0.526858055786505}, 'combined': 0.3242203420224646, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6209850107066381, 'r': 0.7723035952063915, 'f1': 0.6884272997032641}, 'argument': {'p': 0.667533416412618, 'r': 0.6665553454728192, 'f1': 0.6670440224123154}, 'combined': 0.4592113151325139, 'epoch': 6}
Test for Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6771141004481458, 'r': 0.6853172826965657, 'f1': 0.6811909959437049}, 'combined': 0.485746811085448, 'epoch': 6}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5721736529875336, 'r': 0.6033831249686719, 'f1': 0.5873641039518045}, 'combined': 0.3076669115938024, 'epoch': 6}
******************************
Epoch: 9
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 18:46:55.882941: step: 3074/1536, loss: 0.2163812220096588
==================================================
Loss: 0.327
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.648490879540074, 'r': 0.6956278817973921, 'f1': 0.6712328551243699}, 'combined': 0.4422608998716642, 'epoch': 9}
Test Chinese: {'event': {'p': 0.6449207828518173, 'r': 0.7958596894767107, 'f1': 0.7124839124839124}, 'argument': {'p': 0.6649691312335905, 'r': 0.6874373715039405, 'f1': 0.676016613024516}, 'combined': 0.4816509613518301, 'epoch': 9}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6490729913125478, 'r': 0.6768433368436866, 'f1': 0.6626673493130592}, 'combined': 0.4255179921410018, 'epoch': 9}
Test Korean: {'event': {'p': 0.5997352162400706, 'r': 0.7814836112708453, 'f1': 0.6786516853932585}, 'argument': {'p': 0.6646465341090512, 'r': 0.6908261599007829, 'f1': 0.6774835301058294}, 'combined': 0.4597753395324955, 'epoch': 9}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6527473142969521, 'r': 0.692496417075123, 'f1': 0.6720346147905965}, 'combined': 0.4578609719760266, 'epoch': 9}
Test Russian: {'event': {'p': 0.6560232220609579, 'r': 0.7797584818861415, 'f1': 0.7125591171833946}, 'argument': {'p': 0.6626271179607067, 'r': 0.6816378510373533, 'f1': 0.6719980586304527}, 'combined': 0.4788383434066704, 'epoch': 9}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6436969696969698, 'r': 0.6649762083646382, 'f1': 0.6541635870904166}, 'combined': 0.47062128567655864, 'epoch': 9}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.585978835978836, 'r': 0.4282153032153032, 'f1': 0.4948265726043505}, 'combined': 0.2968959435626103, 'epoch': 9}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.558007381519332, 'r': 0.5985897365389199, 'f1': 0.5775865878884314}, 'combined': 0.30254535556060697, 'epoch': 9}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6688731315756482, 'r': 0.6492292804280521, 'f1': 0.6589048285715402}, 'combined': 0.43413822817096803, 'epoch': 7}
Test for Chinese: {'event': {'p': 0.6444547996272134, 'r': 0.7952846463484762, 'f1': 0.711969111969112}, 'argument': {'p': 0.6750973172286543, 'r': 0.6785558075833092, 'f1': 0.6768221443038426}, 'combined': 0.48187646104103693, 'epoch': 7}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7101239669421487, 'r': 0.6455672426746807, 'f1': 0.6763085399449035}, 'combined': 0.48655290643518234, 'epoch': 7}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6771052432339354, 'r': 0.6314742377116485, 'f1': 0.6534941492628983}, 'combined': 0.4196276133998452, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6028243601059136, 'r': 0.7855089131684876, 'f1': 0.6821473158551811}, 'argument': {'p': 0.6708910639130189, 'r': 0.675901112591256, 'f1': 0.6733867695954818}, 'combined': 0.4593489774119492, 'epoch': 7}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6559899322047661, 'r': 0.44020377029530355, 'f1': 0.526858055786505}, 'combined': 0.3242203420224646, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6209850107066381, 'r': 0.7723035952063915, 'f1': 0.6884272997032641}, 'argument': {'p': 0.667533416412618, 'r': 0.6665553454728192, 'f1': 0.6670440224123154}, 'combined': 0.4592113151325139, 'epoch': 6}
Test for Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6771141004481458, 'r': 0.6853172826965657, 'f1': 0.6811909959437049}, 'combined': 0.485746811085448, 'epoch': 6}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5721736529875336, 'r': 0.6033831249686719, 'f1': 0.5873641039518045}, 'combined': 0.3076669115938024, 'epoch': 6}
******************************
Epoch: 10
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 18:52:52.425443: step: 2/1536, loss: 0.13897201418876648
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2023-01-21 19:15:42.291229: step: 3074/1536, loss: 0.0859588161110878
==================================================
Loss: 0.295
--------------------
Dev Chinese: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6671141545300019, 'r': 0.6818082989029094, 'f1': 0.6743811932503724}, 'combined': 0.44039612970439035, 'epoch': 10}
Test Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.662707164683927, 'r': 0.7025148305489001, 'f1': 0.682030634031901}, 'combined': 0.48874473234100707, 'epoch': 10}
Dev Korean: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6715574612632468, 'r': 0.6676643745312859, 'f1': 0.6696052593409699}, 'combined': 0.42845636639597734, 'epoch': 10}
Test Korean: {'event': {'p': 0.5997352162400706, 'r': 0.7814836112708453, 'f1': 0.6786516853932585}, 'argument': {'p': 0.6618063225674397, 'r': 0.6997456945192366, 'f1': 0.6802474220750897}, 'combined': 0.4616510594756789, 'epoch': 10}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6592698639942733, 'r': 0.6919609316303531, 'f1': 0.6752199413489736}, 'combined': 0.4857697419776788, 'epoch': 10}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.5944444444444446, 'r': 0.4572649572649573, 'f1': 0.5169082125603865}, 'combined': 0.3101449275362319, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
New best chinese model...
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6671141545300019, 'r': 0.6818082989029094, 'f1': 0.6743811932503724}, 'combined': 0.44039612970439035, 'epoch': 10}
Test for Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.662707164683927, 'r': 0.7025148305489001, 'f1': 0.682030634031901}, 'combined': 0.48874473234100707, 'epoch': 10}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6592698639942733, 'r': 0.6919609316303531, 'f1': 0.6752199413489736}, 'combined': 0.4857697419776788, 'epoch': 10}
--------------------
Dev for Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6771052432339354, 'r': 0.6314742377116485, 'f1': 0.6534941492628983}, 'combined': 0.4196276133998452, 'epoch': 7}
Test for Korean: {'event': {'p': 0.6028243601059136, 'r': 0.7855089131684876, 'f1': 0.6821473158551811}, 'argument': {'p': 0.6708910639130189, 'r': 0.675901112591256, 'f1': 0.6733867695954818}, 'combined': 0.4593489774119492, 'epoch': 7}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6559899322047661, 'r': 0.44020377029530355, 'f1': 0.526858055786505}, 'combined': 0.3242203420224646, 'epoch': 7}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 11
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 19:21:57.850846: step: 2/1536, loss: 0.17155590653419495
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2023-01-21 19:44:47.250232: step: 3074/1536, loss: 0.07170984148979187
==================================================
Loss: 0.243
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Dev Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6857972148922927, 'r': 0.660676437790011, 'f1': 0.6730024907338544}, 'combined': 0.45852039131303557, 'epoch': 11}
Test Russian: {'event': {'p': 0.6618287373004355, 'r': 0.7866589994249569, 'f1': 0.7188649500788229}, 'argument': {'p': 0.6865001436749074, 'r': 0.6724850888386377, 'f1': 0.6794203484508523}, 'combined': 0.4884114748716584, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6086520433182209, 'r': 0.5201208370173888, 'f1': 0.5609146281560076}, 'combined': 0.29381242427219445, 'epoch': 11}
New best chinese model...
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 12
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 20:13:45.526515: step: 3070/1536, loss: 0.35784950852394104
2023-01-21 20:13:46.385064: step: 3072/1536, loss: 0.42489203810691833
2023-01-21 20:13:47.252018: step: 3074/1536, loss: 0.062218986451625824
==================================================
Loss: 0.203
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6746674421121817, 'r': 0.6623018365016212, 'f1': 0.6684274546712625}, 'combined': 0.43963160862140277, 'epoch': 12}
Test Chinese: {'event': {'p': 0.6491146318732526, 'r': 0.8010350776308223, 'f1': 0.7171171171171172}, 'argument': {'p': 0.6895663436055048, 'r': 0.6829766311068857, 'f1': 0.6862556684304442}, 'combined': 0.4921256865501204, 'epoch': 12}
Dev Korean: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.680412081628976, 'r': 0.6468602168350889, 'f1': 0.6632120736175372}, 'combined': 0.42361450681799656, 'epoch': 12}
Test Korean: {'event': {'p': 0.6050308914386584, 'r': 0.7883841288096607, 'f1': 0.6846441947565542}, 'argument': {'p': 0.6914049951331935, 'r': 0.684361820310105, 'f1': 0.6878653791171533}, 'combined': 0.4709430385865753, 'epoch': 12}
Dev Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6813142823799667, 'r': 0.6573559779446272, 'f1': 0.6691207381762984}, 'combined': 0.45190468845378495, 'epoch': 12}
Test Russian: {'event': {'p': 0.6584421867440735, 'r': 0.7826336975273146, 'f1': 0.7151865475564897}, 'argument': {'p': 0.684829814412095, 'r': 0.6793777252007838, 'f1': 0.682092875101673}, 'combined': 0.48782364845684545, 'epoch': 12}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6408246225319396, 'r': 0.6514167650531286, 'f1': 0.6460772833723653}, 'combined': 0.4648038009873131, 'epoch': 12}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.5812447786131997, 'r': 0.42475579975579975, 'f1': 0.4908289241622575}, 'combined': 0.2944973544973545, 'epoch': 12}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.6208931546372563, 'r': 0.5644483223975056, 'f1': 0.591326813940244}, 'combined': 0.295663406970122, 'epoch': 12}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 13
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:19:38.506566: step: 2/1536, loss: 0.18601851165294647
2023-01-21 20:19:39.402293: step: 4/1536, loss: 0.2860043942928314
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2023-01-21 20:19:43.804653: step: 14/1536, loss: 0.032217998057603836
2023-01-21 20:19:44.692348: step: 16/1536, loss: 0.02530137076973915
2023-01-21 20:19:45.586672: step: 18/1536, loss: 2.3316047191619873
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2023-01-21 20:42:26.427530: step: 3074/1536, loss: 0.38907453417778015
==================================================
Loss: 0.183
--------------------
Dev Chinese: {'event': {'p': 0.5889698231009365, 'r': 0.7536617842876165, 'f1': 0.661214953271028}, 'argument': {'p': 0.6540276253151616, 'r': 0.666992930662819, 'f1': 0.6604466532626387}, 'combined': 0.4366972029750625, 'epoch': 13}
Test Chinese: {'event': {'p': 0.646784715750233, 'r': 0.7981598619896493, 'f1': 0.7145431145431146}, 'argument': {'p': 0.6759930737964827, 'r': 0.6886823635776453, 'f1': 0.6822787236796283}, 'combined': 0.4875175642045427, 'epoch': 13}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6619992474615366, 'r': 0.6543127540222154, 'f1': 0.6581335584252648}, 'combined': 0.42260671305450187, 'epoch': 13}
Test Korean: {'event': {'p': 0.6006178287731686, 'r': 0.7826336975273146, 'f1': 0.6796504369538078}, 'argument': {'p': 0.6756678467664798, 'r': 0.6880612108036368, 'f1': 0.6818082142732772}, 'combined': 0.46339125074952825, 'epoch': 13}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6598543723813426, 'r': 0.6651679873858776, 'f1': 0.6625005255334369}, 'combined': 0.45136534321209826, 'epoch': 13}
Test Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6729929999876355, 'r': 0.6841630082861855, 'f1': 0.6785320370245708}, 'combined': 0.4849204258294357, 'epoch': 13}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.6624307592049528, 'r': 0.6788546623257368, 'f1': 0.670542156256442}, 'combined': 0.4920525175406985, 'epoch': 13}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.606873822975518, 'r': 0.45904558404558404, 'f1': 0.5227088402270884}, 'combined': 0.31362530413625306, 'epoch': 13}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.577436173421175, 'r': 0.5564384943876777, 'f1': 0.5667429109504124}, 'combined': 0.2833714554752062, 'epoch': 13}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 14
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 20:48:21.902600: step: 2/1536, loss: 0.026466134935617447
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2023-01-21 21:11:10.647386: step: 3074/1536, loss: 0.06093994900584221
==================================================
Loss: 0.169
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6668690069233995, 'r': 0.6688260693061534, 'f1': 0.6678461043694411}, 'combined': 0.440029442598557, 'epoch': 14}
Test Chinese: {'event': {'p': 0.6435228331780056, 'r': 0.7941345600920069, 'f1': 0.7109395109395109}, 'argument': {'p': 0.6955486087584779, 'r': 0.6675271899010178, 'f1': 0.6812498742927112}, 'combined': 0.4843274524572634, 'epoch': 14}
Dev Korean: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6712377253120072, 'r': 0.6571320049974796, 'f1': 0.6641099720955853}, 'combined': 0.4241880229466706, 'epoch': 14}
Test Korean: {'event': {'p': 0.5975286849073257, 'r': 0.7786083956296722, 'f1': 0.6761548064918851}, 'argument': {'p': 0.6959283516547969, 'r': 0.6670889294734501, 'f1': 0.6812035408844388}, 'combined': 0.4605990483683046, 'epoch': 14}
Dev Russian: {'event': {'p': 0.6102783725910065, 'r': 0.758988015978695, 'f1': 0.6765578635014837}, 'argument': {'p': 0.6693319516307507, 'r': 0.6703105071155909, 'f1': 0.6698208719752581}, 'combined': 0.45317257807228145, 'epoch': 14}
Test Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6920726503187844, 'r': 0.6659431321737385, 'f1': 0.6787565128280965}, 'combined': 0.48401081865881596, 'epoch': 14}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.6554866850321396, 'r': 0.5958969863928542, 'f1': 0.6242730333639425}, 'combined': 0.4580996359936845, 'epoch': 14}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5656351022483745, 'r': 0.4298826777087646, 'f1': 0.4885030428508689}, 'combined': 0.3006172571389962, 'epoch': 14}
Russian: {'event': {'p': 0.3958333333333333, 'r': 0.5277777777777778, 'f1': 0.45238095238095233}, 'argument': {'p': 0.5552900380674924, 'r': 0.5350976730468563, 'f1': 0.5450068892143908}, 'combined': 0.2465507355969863, 'epoch': 14}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 15
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 21:39:52.262315: step: 3074/1536, loss: 0.03206019103527069
==================================================
Loss: 0.163
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6701043763785898, 'r': 0.6769873266862202, 'f1': 0.6735282673527869}, 'combined': 0.4445601297363605, 'epoch': 15}
Test Chinese: {'event': {'p': 0.6453867660764212, 'r': 0.7964347326049454, 'f1': 0.712998712998713}, 'argument': {'p': 0.6704796262338404, 'r': 0.6899370411296671, 'f1': 0.6800691884465069}, 'combined': 0.48488845611243864, 'epoch': 15}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6794765206355626, 'r': 0.6646945803751806, 'f1': 0.6720042715083389}, 'combined': 0.43227454837682505, 'epoch': 15}
Test Korean: {'event': {'p': 0.6019417475728155, 'r': 0.7843588269120184, 'f1': 0.6811485642946317}, 'argument': {'p': 0.6722921065631194, 'r': 0.6912524344070674, 'f1': 0.6816404471301724}, 'combined': 0.4642984119278677, 'epoch': 15}
Dev Russian: {'event': {'p': 0.6102783725910065, 'r': 0.758988015978695, 'f1': 0.6765578635014837}, 'argument': {'p': 0.6774014038176381, 'r': 0.6739351685641853, 'f1': 0.6756638406711656}, 'combined': 0.4571256844896907, 'epoch': 15}
Test Russian: {'event': {'p': 0.6545718432510885, 'r': 0.7780333525014376, 'f1': 0.7109826589595376}, 'argument': {'p': 0.6703264813338511, 'r': 0.68678332095166, 'f1': 0.6784551201339711}, 'combined': 0.48236982529756334, 'epoch': 15}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6482471776589424, 'r': 0.6375323482761499, 'f1': 0.6428451178451179}, 'combined': 0.4624785020468474, 'epoch': 15}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.5506456820016142, 'r': 0.41124171187462333, 'f1': 0.4708419599723948}, 'combined': 0.2969926209056644, 'epoch': 15}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.5834421794271809, 'r': 0.5622261001752834, 'f1': 0.5726376946229738}, 'combined': 0.2863188473114869, 'epoch': 15}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.664938940715184, 'r': 0.6800511893678018, 'f1': 0.6724101647681636}, 'combined': 0.45572249742744375, 'epoch': 10}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6589436062839221, 'r': 0.698174585958839, 'f1': 0.6779920601126339}, 'combined': 0.48595962690154115, 'epoch': 10}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6070196264640708, 'r': 0.5959829059829059, 'f1': 0.6014506390653178}, 'combined': 0.3150455728437379, 'epoch': 10}
******************************
Epoch: 16
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 21:45:45.073871: step: 2/1536, loss: 0.07423745095729828
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==================================================
Loss: 0.129
--------------------
Dev Chinese: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6713272915181182, 'r': 0.6624681337121606, 'f1': 0.6668682910578503}, 'combined': 0.4425013893935268, 'epoch': 16}
Test Chinese: {'event': {'p': 0.6453867660764212, 'r': 0.7964347326049454, 'f1': 0.712998712998713}, 'argument': {'p': 0.673402449614439, 'r': 0.6915590344333948, 'f1': 0.6823599834618106}, 'combined': 0.48652179001009405, 'epoch': 16}
Dev Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6737903669786948, 'r': 0.6498832504095738, 'f1': 0.6616209132440999}, 'combined': 0.42709390775666695, 'epoch': 16}
Test Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.6776683698735072, 'r': 0.6907756305056042, 'f1': 0.6841592280625157}, 'combined': 0.4656724233953603, 'epoch': 16}
Dev Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.6677856042262822, 'r': 0.6512289363529034, 'f1': 0.6594033581481281}, 'combined': 0.48387872324538905, 'epoch': 16}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.5871647509578545, 'r': 0.43660968660968663, 'f1': 0.5008169934640524}, 'combined': 0.3004901960784314, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 17
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.119
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.670925186373973, 'r': 0.6551734578604241, 'f1': 0.6629557706486697}, 'combined': 0.4375817878697411, 'epoch': 17}
Test Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.6858359123204645, 'r': 0.6795159226847469, 'f1': 0.6826612904050432}, 'combined': 0.4891966621589808, 'epoch': 17}
Dev Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6776456837029173, 'r': 0.642290256727113, 'f1': 0.6594944600323035}, 'combined': 0.4249743462722318, 'epoch': 17}
Test Korean: {'event': {'p': 0.6037069726390115, 'r': 0.7866589994249569, 'f1': 0.6831460674157304}, 'argument': {'p': 0.6853073059199262, 'r': 0.6801878625480285, 'f1': 0.6827379874587403}, 'combined': 0.46640977120776866, 'epoch': 17}
Dev Russian: {'event': {'p': 0.6167023554603854, 'r': 0.7669773635153129, 'f1': 0.6836795252225518}, 'argument': {'p': 0.6711958664276898, 'r': 0.65300227883954, 'f1': 0.6619740888347361}, 'combined': 0.4525781307641637, 'epoch': 17}
Test Russian: {'event': {'p': 0.6594097726173198, 'r': 0.7837837837837838, 'f1': 0.7162375197057278}, 'argument': {'p': 0.680595113589498, 'r': 0.6749470213605395, 'f1': 0.6777593006162084}, 'combined': 0.48543664043084184, 'epoch': 17}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.6603259103259103, 'r': 0.6057535210427772, 'f1': 0.631863586604966}, 'combined': 0.4636696822568816, 'epoch': 17}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.6278735632183907, 'r': 0.4609704641350211, 'f1': 0.5316301703163017}, 'combined': 0.318978102189781, 'epoch': 17}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5507233172439292, 'r': 0.5507233172439292, 'f1': 0.5507233172439292}, 'combined': 0.28847411855634386, 'epoch': 17}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 18
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 23:06:06.211116: step: 3074/1536, loss: 0.04513651132583618
==================================================
Loss: 0.110
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6780978110807978, 'r': 0.6531860619595341, 'f1': 0.6654088550351241}, 'combined': 0.43764624460838186, 'epoch': 18}
Test Chinese: {'event': {'p': 0.6500465983224604, 'r': 0.8021851638872916, 'f1': 0.718146718146718}, 'argument': {'p': 0.6884845751089845, 'r': 0.6802575487761697, 'f1': 0.6843463370652245}, 'combined': 0.4914610760391187, 'epoch': 18}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6864351846547855, 'r': 0.6326362006615222, 'f1': 0.6584385834679589}, 'combined': 0.4235482620722545, 'epoch': 18}
Test Korean: {'event': {'p': 0.6059135039717564, 'r': 0.78953421506613, 'f1': 0.6856429463171035}, 'argument': {'p': 0.6883093049180167, 'r': 0.6826980877583589, 'f1': 0.6854922136291299}, 'combined': 0.47000290103011, 'epoch': 18}
Dev Russian: {'event': {'p': 0.6124197002141327, 'r': 0.7616511318242344, 'f1': 0.6789317507418398}, 'argument': {'p': 0.6762707015355941, 'r': 0.653430428209135, 'f1': 0.6646544012104868}, 'combined': 0.451254976252105, 'epoch': 18}
Test Russian: {'event': {'p': 0.660377358490566, 'r': 0.7849338700402531, 'f1': 0.717288491854966}, 'argument': {'p': 0.6878212454068977, 'r': 0.6790121813431887, 'f1': 0.6833883267376667}, 'combined': 0.49018658223694966, 'epoch': 18}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.7293435730935732, 'r': 0.6268738148903439, 'f1': 0.6742376142376144}, 'combined': 0.4947642924621344, 'epoch': 18}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6651234567901234, 'r': 0.4546413502109704, 'f1': 0.5401002506265663}, 'combined': 0.33236938500096386, 'epoch': 18}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5707858427711221, 'r': 0.5604079183571017, 'f1': 0.5655492754062494}, 'combined': 0.29624009664136874, 'epoch': 18}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 19
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-21 23:34:44.577049: step: 3074/1536, loss: 0.010728538036346436
==================================================
Loss: 0.102
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6831458698286844, 'r': 0.6526170067813952, 'f1': 0.6675325696939711}, 'combined': 0.4390430335720861, 'epoch': 19}
Test Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6894596047540361, 'r': 0.6736938049183636, 'f1': 0.6814855337168325}, 'combined': 0.490809915917554, 'epoch': 19}
Dev Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6858867836953956, 'r': 0.6357967148933457, 'f1': 0.659892575382737}, 'combined': 0.425230889459544, 'epoch': 19}
Test Korean: {'event': {'p': 0.6045895851721095, 'r': 0.7878090856814262, 'f1': 0.6841448189762797}, 'argument': {'p': 0.6887317807817449, 'r': 0.6751630160788437, 'f1': 0.681879903717364}, 'combined': 0.46650460329227894, 'epoch': 19}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6776197048354086, 'r': 0.6497995410005237, 'f1': 0.6634180942550257}, 'combined': 0.4519904879553529, 'epoch': 19}
Test Russian: {'event': {'p': 0.6589259796806967, 'r': 0.7832087406555491, 'f1': 0.7157120336311087}, 'argument': {'p': 0.6862861951999386, 'r': 0.6708292989116517, 'f1': 0.6784697237284131}, 'combined': 0.485588945726799, 'epoch': 19}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6863013249376886, 'r': 0.6239102953978988, 'f1': 0.6536203094644654}, 'combined': 0.46082582969437125, 'epoch': 19}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6095641646489104, 'r': 0.455244122965642, 'f1': 0.5212215320910973}, 'combined': 0.32075171205605985, 'epoch': 19}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5626071196397874, 'r': 0.5932947807110486, 'f1': 0.5775435918426136}, 'combined': 0.3025228338223214, 'epoch': 19}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 20
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-21 23:40:36.099126: step: 2/1536, loss: 0.014285526238381863
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2023-01-22 00:03:21.900041: step: 3072/1536, loss: 0.018747199326753616
2023-01-22 00:03:22.740991: step: 3074/1536, loss: 0.027460405603051186
==================================================
Loss: 0.103
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6816289367011696, 'r': 0.6606403624039195, 'f1': 0.6709705542211067}, 'combined': 0.4413042313393494, 'epoch': 20}
Test Chinese: {'event': {'p': 0.6481826654240447, 'r': 0.7998849913743531, 'f1': 0.716087516087516}, 'argument': {'p': 0.6874998761113762, 'r': 0.6832763273844121, 'f1': 0.6853815950826181}, 'combined': 0.49079320399481163, 'epoch': 20}
Dev Korean: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6894327128478229, 'r': 0.643537047448402, 'f1': 0.6656947601599488}, 'combined': 0.42670807955892537, 'epoch': 20}
Test Korean: {'event': {'p': 0.6050308914386584, 'r': 0.7883841288096607, 'f1': 0.6846441947565542}, 'argument': {'p': 0.6858233663456264, 'r': 0.6834930117199192, 'f1': 0.6846562060897625}, 'combined': 0.4687458969034029, 'epoch': 20}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.6781344851598387, 'r': 0.6567563452317792, 'f1': 0.6672742308351186}, 'combined': 0.45224164487460256, 'epoch': 20}
Test Russian: {'event': {'p': 0.6589259796806967, 'r': 0.7832087406555491, 'f1': 0.7157120336311087}, 'argument': {'p': 0.6819215979284783, 'r': 0.6779075165002288, 'f1': 0.6799086326281163}, 'combined': 0.48661879014161546, 'epoch': 20}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6797999297999299, 'r': 0.6236181174197704, 'f1': 0.6504982086878639}, 'combined': 0.45862463634108386, 'epoch': 20}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5873655913978494, 'r': 0.4609704641350211, 'f1': 0.516548463356974}, 'combined': 0.3178759774504455, 'epoch': 20}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6008931546372562, 'r': 0.5462665042156875, 'f1': 0.572279194892625}, 'combined': 0.2997652925628036, 'epoch': 20}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 21
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 00:09:15.661983: step: 2/1536, loss: 0.11472132056951523
2023-01-22 00:09:16.544743: step: 4/1536, loss: 0.00016416283324360847
2023-01-22 00:09:17.467916: step: 6/1536, loss: 8.196369162760675e-05
2023-01-22 00:09:18.336547: step: 8/1536, loss: 0.010345619171857834
2023-01-22 00:09:19.202516: step: 10/1536, loss: 0.08423120528459549
2023-01-22 00:09:20.180238: step: 12/1536, loss: 0.0006750410539098084
2023-01-22 00:09:21.091912: step: 14/1536, loss: 0.09249551594257355
2023-01-22 00:09:21.976467: step: 16/1536, loss: 0.05824914574623108
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2023-01-22 00:32:02.843535: step: 3074/1536, loss: 0.01508427131921053
==================================================
Loss: 0.092
--------------------
Dev Chinese: {'event': {'p': 0.5848074921956296, 'r': 0.748335552596538, 'f1': 0.6565420560747663}, 'argument': {'p': 0.6603486717972549, 'r': 0.6792296088940971, 'f1': 0.6696560798926987}, 'combined': 0.43965737955572043, 'epoch': 21}
Test Chinese: {'event': {'p': 0.6500465983224604, 'r': 0.8021851638872916, 'f1': 0.718146718146718}, 'argument': {'p': 0.676257625336289, 'r': 0.6928812337433264, 'f1': 0.6844685105005744}, 'combined': 0.49154881449075993, 'epoch': 21}
Dev Korean: {'event': {'p': 0.5536945812807882, 'r': 0.748335552596538, 'f1': 0.636466591166478}, 'argument': {'p': 0.667732548389382, 'r': 0.6619345812543078, 'f1': 0.664820923905126}, 'combined': 0.423136307174044, 'epoch': 21}
Test Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6753650513768515, 'r': 0.6923351749089015, 'f1': 0.6837448323630836}, 'combined': 0.4660732565171581, 'epoch': 21}
Dev Russian: {'event': {'p': 0.6124197002141327, 'r': 0.7616511318242344, 'f1': 0.6789317507418398}, 'argument': {'p': 0.6593495931048441, 'r': 0.6762312578880698, 'f1': 0.6676837338627601}, 'combined': 0.45331168637329233, 'epoch': 21}
Test Russian: {'event': {'p': 0.6627963231736816, 'r': 0.7878090856814262, 'f1': 0.7199159222280609}, 'argument': {'p': 0.6738295325762854, 'r': 0.6890006166141998, 'f1': 0.6813306320141103}, 'combined': 0.4905007702886658, 'epoch': 21}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6429811507936507, 'r': 0.6801784074511347, 'f1': 0.6610569261171669}, 'combined': 0.46606891193872196, 'epoch': 21}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5837301587301588, 'r': 0.44333936106088007, 'f1': 0.5039397053785544}, 'combined': 0.310116741771418, 'epoch': 21}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.545522468877732, 'r': 0.5554410592209635, 'f1': 0.5504370857144684}, 'combined': 0.2752185428572342, 'epoch': 21}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 22
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:00:44.094507: step: 3072/1536, loss: 0.027616281062364578
2023-01-22 01:00:44.989753: step: 3074/1536, loss: 0.015069089829921722
==================================================
Loss: 0.089
--------------------
Dev Chinese: {'event': {'p': 0.5816857440166493, 'r': 0.7443408788282291, 'f1': 0.6530373831775701}, 'argument': {'p': 0.6756600523863261, 'r': 0.6667437173841605, 'f1': 0.6711722734405867}, 'combined': 0.4383005851089813, 'epoch': 22}
Test Chinese: {'event': {'p': 0.6491146318732526, 'r': 0.8010350776308223, 'f1': 0.7171171171171172}, 'argument': {'p': 0.6904434991407834, 'r': 0.6862004185724891, 'f1': 0.6883154198626098}, 'combined': 0.49360276955913285, 'epoch': 22}
Dev Korean: {'event': {'p': 0.5517241379310345, 'r': 0.7456724367509987, 'f1': 0.6342015855039639}, 'argument': {'p': 0.6803012499217982, 'r': 0.6532270323055183, 'f1': 0.6664893013261176}, 'combined': 0.42268857162245294, 'epoch': 22}
Test Korean: {'event': {'p': 0.6050308914386584, 'r': 0.7883841288096607, 'f1': 0.6846441947565542}, 'argument': {'p': 0.6856396775093685, 'r': 0.6872704888388166, 'f1': 0.6864541145944806}, 'combined': 0.4699768245238616, 'epoch': 22}
Dev Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.6702974087304732, 'r': 0.6678421068669915, 'f1': 0.6690675052282155}, 'combined': 0.4518687364686702, 'epoch': 22}
Test Russian: {'event': {'p': 0.6584421867440735, 'r': 0.7826336975273146, 'f1': 0.7151865475564897}, 'argument': {'p': 0.6847701338184774, 'r': 0.6812110374888595, 'f1': 0.6829859489987314}, 'combined': 0.48846236289399547, 'epoch': 22}
Chinese: {'event': {'p': 0.611764705882353, 'r': 0.9629629629629629, 'f1': 0.748201438848921}, 'argument': {'p': 0.6542766373411534, 'r': 0.6704983721512646, 'f1': 0.6622881880024737}, 'combined': 0.4955249751960955, 'epoch': 22}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5824449182658138, 'r': 0.4939722724532851, 'f1': 0.5345727332028701}, 'combined': 0.32896783581715083, 'epoch': 22}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5768240250527701, 'r': 0.545360896413528, 'f1': 0.5606513888363372}, 'combined': 0.29367453700951, 'epoch': 22}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6857402651218847, 'r': 0.666636064187404, 'f1': 0.6760532279194192}, 'combined': 0.4478062853157836, 'epoch': 11}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6873678404141529, 'r': 0.679391558306958, 'f1': 0.6833564249362811}, 'combined': 0.4921573428498622, 'epoch': 11}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7254269972451789, 'r': 0.6594790884047081, 'f1': 0.6908828545192179}, 'combined': 0.4970380248339697, 'epoch': 11}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 23
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 01:06:38.006375: step: 2/1536, loss: 0.01074922177940607
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2023-01-22 01:29:24.747277: step: 3074/1536, loss: 0.24003179371356964
==================================================
Loss: 0.088
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Dev Korean: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.69151274727679, 'r': 0.6404749034112093, 'f1': 0.6650160154126906}, 'combined': 0.4262730064819739, 'epoch': 23}
Test Korean: {'event': {'p': 0.6059135039717564, 'r': 0.78953421506613, 'f1': 0.6856429463171035}, 'argument': {'p': 0.6944967363168338, 'r': 0.6822298102677593, 'f1': 0.6883086228335447}, 'combined': 0.4719339521350595, 'epoch': 23}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6851804092976093, 'r': 0.6575724074577788, 'f1': 0.6710925877980322}, 'combined': 0.4572191636748611, 'epoch': 23}
Test Russian: {'event': {'p': 0.6608611514271892, 'r': 0.7855089131684876, 'f1': 0.7178139779295848}, 'argument': {'p': 0.6931269139803251, 'r': 0.6777561161650996, 'f1': 0.6853553438165654}, 'combined': 0.49195764564026706, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.6041479484875711, 'r': 0.40531444645368697, 'f1': 0.4851491101491102}, 'combined': 0.2910894660894661, 'epoch': 23}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5678048063191957, 'r': 0.5058624638116471, 'f1': 0.5350468367238574}, 'combined': 0.2802626287601158, 'epoch': 23}
New best chinese model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 24
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 01:58:12.370925: step: 3074/1536, loss: 0.0069528804160654545
==================================================
Loss: 0.095
--------------------
Dev Chinese: {'event': {'p': 0.5827263267429761, 'r': 0.7456724367509987, 'f1': 0.6542056074766356}, 'argument': {'p': 0.6735747788282976, 'r': 0.6641852551322318, 'f1': 0.6688470651978072}, 'combined': 0.43756350059669635, 'epoch': 24}
Test Chinese: {'event': {'p': 0.6505125815470643, 'r': 0.8027602070155262, 'f1': 0.7186615186615186}, 'argument': {'p': 0.680219264512455, 'r': 0.6830051454592636, 'f1': 0.6816093583772624}, 'combined': 0.4898464166253067, 'epoch': 24}
Dev Korean: {'event': {'p': 0.5566502463054187, 'r': 0.7523302263648469, 'f1': 0.6398640996602492}, 'argument': {'p': 0.6756335921822139, 'r': 0.6462794896978309, 'f1': 0.660630625664474}, 'combined': 0.4227138204987857, 'epoch': 24}
Test Korean: {'event': {'p': 0.6006178287731686, 'r': 0.7826336975273146, 'f1': 0.6796504369538078}, 'argument': {'p': 0.6820089677546672, 'r': 0.6857142796338097, 'f1': 0.6838566046466541}, 'combined': 0.4647834401618458, 'epoch': 24}
Dev Russian: {'event': {'p': 0.6092077087794433, 'r': 0.7576564580559254, 'f1': 0.6753709198813057}, 'argument': {'p': 0.671670712845327, 'r': 0.6647767319216946, 'f1': 0.6682059412978567}, 'combined': 0.45128686124448725, 'epoch': 24}
Test Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6798666562512843, 'r': 0.6798666562512843, 'f1': 0.6798666562512843}, 'combined': 0.48587422622267284, 'epoch': 24}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6959325396825395, 'r': 0.6441689623507804, 'f1': 0.669051025274201}, 'combined': 0.4813316728591374, 'epoch': 24}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5794626593806922, 'r': 0.44743319268635723, 'f1': 0.5049603174603174}, 'combined': 0.3107448107448107, 'epoch': 24}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.5648910590985606, 'r': 0.5546203125694958, 'f1': 0.5597085723178399}, 'combined': 0.27985428615891994, 'epoch': 24}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 25
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 02:04:05.505286: step: 2/1536, loss: 0.008307009935379028
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2023-01-22 02:26:51.501104: step: 3074/1536, loss: 0.07477180659770966
==================================================
Loss: 0.085
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6721870273271567, 'r': 0.6711999685498775, 'f1': 0.6716931353158949}, 'combined': 0.44177948035379533, 'epoch': 25}
Test Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.6849119815870489, 'r': 0.6928624791541229, 'f1': 0.6888642910530977}, 'combined': 0.4936417467932623, 'epoch': 25}
Dev Korean: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.676484971564987, 'r': 0.6524474345043022, 'f1': 0.6642488092886176}, 'combined': 0.42578122996303247, 'epoch': 25}
Test Korean: {'event': {'p': 0.6001765225066196, 'r': 0.78205865439908, 'f1': 0.6791510611735331}, 'argument': {'p': 0.6861343686985784, 'r': 0.6938280262476959, 'f1': 0.6899597503791077}, 'combined': 0.468586896636997, 'epoch': 25}
Dev Russian: {'event': {'p': 0.6134903640256959, 'r': 0.762982689747004, 'f1': 0.6801186943620178}, 'argument': {'p': 0.6739674427924579, 'r': 0.6685282337897308, 'f1': 0.6712368196356561}, 'combined': 0.4565207093783156, 'epoch': 25}
Test Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6814093026096971, 'r': 0.6901483473918665, 'f1': 0.6857509840625228}, 'combined': 0.48899846840401645, 'epoch': 25}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6656097313992051, 'r': 0.6271033833017304, 'f1': 0.6457830585490161}, 'combined': 0.4645921284525295, 'epoch': 25}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.612037037037037, 'r': 0.46483825597749645, 'f1': 0.5283772981614708}, 'combined': 0.3170263788968825, 'epoch': 25}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6043614805811881, 'r': 0.5604079183571017, 'f1': 0.5815553869743509}, 'combined': 0.30462425031989815, 'epoch': 25}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 26
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 02:55:22.699540: step: 3058/1536, loss: 0.004173720255494118
2023-01-22 02:55:23.567886: step: 3060/1536, loss: 0.0001482407096773386
2023-01-22 02:55:24.419934: step: 3062/1536, loss: 0.31496143341064453
2023-01-22 02:55:25.307205: step: 3064/1536, loss: 0.010464857332408428
2023-01-22 02:55:26.209154: step: 3066/1536, loss: 0.001446800772100687
2023-01-22 02:55:27.125425: step: 3068/1536, loss: 0.001617965754121542
2023-01-22 02:55:28.000083: step: 3070/1536, loss: 0.25855109095573425
2023-01-22 02:55:28.916528: step: 3072/1536, loss: 0.05887962132692337
2023-01-22 02:55:29.753879: step: 3074/1536, loss: 0.01761470176279545
==================================================
Loss: 0.071
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6677944834563794, 'r': 0.6619194586752382, 'f1': 0.6648439923659977}, 'combined': 0.4388281024378372, 'epoch': 26}
Test Chinese: {'event': {'p': 0.6491146318732526, 'r': 0.8010350776308223, 'f1': 0.7171171171171172}, 'argument': {'p': 0.6883107305261595, 'r': 0.6894857232378805, 'f1': 0.6888977258617609}, 'combined': 0.4940203511585241, 'epoch': 26}
Dev Korean: {'event': {'p': 0.5576354679802956, 'r': 0.7536617842876165, 'f1': 0.6409966024915064}, 'argument': {'p': 0.6787016931680313, 'r': 0.6487228348890668, 'f1': 0.6633737393423186}, 'combined': 0.42522031310051234, 'epoch': 26}
Test Korean: {'event': {'p': 0.6045895851721095, 'r': 0.7878090856814262, 'f1': 0.6841448189762797}, 'argument': {'p': 0.688836323528616, 'r': 0.6879010399936145, 'f1': 0.6883683640696973}, 'combined': 0.4709436498254608, 'epoch': 26}
Dev Russian: {'event': {'p': 0.6134903640256959, 'r': 0.762982689747004, 'f1': 0.6801186943620178}, 'argument': {'p': 0.6713368217622999, 'r': 0.6600166260874224, 'f1': 0.6656285973998108}, 'combined': 0.4527064525935805, 'epoch': 26}
Test Russian: {'event': {'p': 0.6608611514271892, 'r': 0.7855089131684876, 'f1': 0.7178139779295848}, 'argument': {'p': 0.6823006014805142, 'r': 0.6837191058494966, 'f1': 0.6830091171622178}, 'combined': 0.4902734913523854, 'epoch': 26}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.7135007482229704, 'r': 0.6368436430419901, 'f1': 0.6729963389352035}, 'combined': 0.4938534285711566, 'epoch': 26}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6197751322751323, 'r': 0.4707152903355435, 'f1': 0.5350576681511934}, 'combined': 0.3374979137569066, 'epoch': 26}
Russian: {'event': {'p': 0.4791666666666667, 'r': 0.6388888888888888, 'f1': 0.5476190476190476}, 'argument': {'p': 0.5265696483866836, 'r': 0.5648656228148061, 'f1': 0.5450457764002514}, 'combined': 0.29847744898109, 'epoch': 26}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 27
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 03:01:20.925650: step: 2/1536, loss: 0.0003977662418037653
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2023-01-22 03:24:05.333150: step: 3074/1536, loss: 0.0002680070174392313
==================================================
Loss: 0.067
--------------------
Dev Chinese: {'event': {'p': 0.5837669094693028, 'r': 0.7470039946737683, 'f1': 0.655373831775701}, 'argument': {'p': 0.6550640583742495, 'r': 0.670912382367175, 'f1': 0.662893509271153}, 'combined': 0.4344430592302767, 'epoch': 27}
Test Chinese: {'event': {'p': 0.6463187325256291, 'r': 0.7975848188614146, 'f1': 0.714028314028314}, 'argument': {'p': 0.6765194439094286, 'r': 0.6938542434629672, 'f1': 0.6850772037834728}, 'combined': 0.4891645207967448, 'epoch': 27}
Dev Korean: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.6596408012318956, 'r': 0.6577287989094843, 'f1': 0.6586834125508624}, 'combined': 0.4199759470737661, 'epoch': 27}
Test Korean: {'event': {'p': 0.5997352162400706, 'r': 0.7814836112708453, 'f1': 0.6786516853932585}, 'argument': {'p': 0.672934941576141, 'r': 0.6946498938348818, 'f1': 0.6836200192043764}, 'combined': 0.4639398782016218, 'epoch': 27}
Dev Russian: {'event': {'p': 0.6124197002141327, 'r': 0.7616511318242344, 'f1': 0.6789317507418398}, 'argument': {'p': 0.655596794127813, 'r': 0.6695252241862061, 'f1': 0.6624878079116864}, 'combined': 0.44978400727060486, 'epoch': 27}
Test Russian: {'event': {'p': 0.656507014997581, 'r': 0.780333525014376, 'f1': 0.7130846032580136}, 'argument': {'p': 0.6738210816157737, 'r': 0.6861911949949341, 'f1': 0.6799498815686886}, 'combined': 0.48486179153374165, 'epoch': 27}
Chinese: {'event': {'p': 0.6, 'r': 0.9444444444444444, 'f1': 0.7338129496402879}, 'argument': {'p': 0.6710336119818878, 'r': 0.6433049503297437, 'f1': 0.656876784724886}, 'combined': 0.482024690949197, 'epoch': 27}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5829059829059828, 'r': 0.4796061884669479, 'f1': 0.5262345679012345}, 'combined': 0.3238366571699904, 'epoch': 27}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5824464649844835, 'r': 0.6142162721654554, 'f1': 0.5979096454707974}, 'combined': 0.3131907666751796, 'epoch': 27}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6678074892390137, 'r': 0.6624219449709571, 'f1': 0.6651038151935116}, 'combined': 0.45392841986500787, 'epoch': 16}
Test for Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6694627142179117, 'r': 0.6886828474867068, 'f1': 0.678936781614346}, 'combined': 0.4844961373790236, 'epoch': 16}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6180881691408007, 'r': 0.6180881691408007, 'f1': 0.6180881691408007}, 'combined': 0.3237604695499432, 'epoch': 16}
******************************
Epoch: 28
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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==================================================
Loss: 0.064
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6729474364320001, 'r': 0.6793658639254821, 'f1': 0.6761414184230976}, 'combined': 0.4478647012218416, 'epoch': 28}
Test Chinese: {'event': {'p': 0.6481826654240447, 'r': 0.7998849913743531, 'f1': 0.716087516087516}, 'argument': {'p': 0.6834727835739078, 'r': 0.6951361416553739, 'f1': 0.6892551252285094}, 'combined': 0.49356699057547304, 'epoch': 28}
Dev Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.6738753110332779, 'r': 0.6616762648523713, 'f1': 0.667720074359609}, 'combined': 0.4302748837039836, 'epoch': 28}
Test Korean: {'event': {'p': 0.6010591350397175, 'r': 0.7832087406555491, 'f1': 0.6801498127340824}, 'argument': {'p': 0.6813181591464269, 'r': 0.6993632580983526, 'f1': 0.6902227865615443}, 'combined': 0.4694548990246309, 'epoch': 28}
Dev Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Chinese: {'event': {'p': 0.5647058823529412, 'r': 0.8888888888888888, 'f1': 0.6906474820143885}, 'argument': {'p': 0.7176582337132794, 'r': 0.646485516320227, 'f1': 0.6802151954325867}, 'combined': 0.4697889119534412, 'epoch': 28}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.586021505376344, 'r': 0.4599156118143459, 'f1': 0.5153664302600471}, 'combined': 0.30921985815602826, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
New best russian model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5615763546798029, 'r': 0.758988015978695, 'f1': 0.6455266138165345}, 'argument': {'p': 0.6851062613996073, 'r': 0.6508757530458252, 'f1': 0.6675524804725472}, 'combined': 0.4309228922642717, 'epoch': 11}
Test for Korean: {'event': {'p': 0.6063548102383054, 'r': 0.7901092581943646, 'f1': 0.6861423220973782}, 'argument': {'p': 0.686829564598077, 'r': 0.6789081924147884, 'f1': 0.6828459062568393}, 'combined': 0.46852947575375636, 'epoch': 11}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6208949096880131, 'r': 0.46169108669108666, 'f1': 0.5295868347338935}, 'combined': 0.3340470803706097, 'epoch': 11}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 29
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 04:21:30.683684: step: 3074/1536, loss: 0.0010621940018609166
==================================================
Loss: 0.061
--------------------
Dev Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6482398312923923, 'r': 0.6867350118896677, 'f1': 0.6669324002972635}, 'combined': 0.439427422625767, 'epoch': 29}
Test Chinese: {'event': {'p': 0.6472506989748369, 'r': 0.7987349051178838, 'f1': 0.7150579150579152}, 'argument': {'p': 0.6731712107595392, 'r': 0.703508732036514, 'f1': 0.6880057015475625}, 'combined': 0.49196392249655824, 'epoch': 29}
Dev Korean: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6587320085968769, 'r': 0.6668350796143493, 'f1': 0.6627587774376343}, 'combined': 0.42332497222517074, 'epoch': 29}
Test Korean: {'event': {'p': 0.6019417475728155, 'r': 0.7843588269120184, 'f1': 0.6811485642946317}, 'argument': {'p': 0.67064108513621, 'r': 0.7038772013717453, 'f1': 0.6868573154160853}, 'combined': 0.4678518742709315, 'epoch': 29}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.6545276387125546, 'r': 0.6823594591270181, 'f1': 0.6681538422464125}, 'combined': 0.452837796940892, 'epoch': 29}
Test Russian: {'event': {'p': 0.6589259796806967, 'r': 0.7832087406555491, 'f1': 0.7157120336311087}, 'argument': {'p': 0.6686949426351443, 'r': 0.6973863519385207, 'f1': 0.6827393486118988}, 'combined': 0.48864476763500053, 'epoch': 29}
Chinese: {'event': {'p': 0.611764705882353, 'r': 0.9629629629629629, 'f1': 0.748201438848921}, 'argument': {'p': 0.6444433258386746, 'r': 0.6870511490346201, 'f1': 0.6650655122655121}, 'combined': 0.4976029732058509, 'epoch': 29}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6032356532356533, 'r': 0.4963331324090818, 'f1': 0.5445877425044091}, 'combined': 0.34350919142585806, 'epoch': 29}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5697955714725923, 'r': 0.5387158130286327, 'f1': 0.5538199947023327}, 'combined': 0.2900961877012219, 'epoch': 29}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6587320085968769, 'r': 0.6668350796143493, 'f1': 0.6627587774376343}, 'combined': 0.42332497222517074, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6019417475728155, 'r': 0.7843588269120184, 'f1': 0.6811485642946317}, 'argument': {'p': 0.67064108513621, 'r': 0.7038772013717453, 'f1': 0.6868573154160853}, 'combined': 0.4678518742709315, 'epoch': 29}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6032356532356533, 'r': 0.4963331324090818, 'f1': 0.5445877425044091}, 'combined': 0.34350919142585806, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 30
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 04:50:17.692509: step: 3072/1536, loss: 0.008480806834995747
2023-01-22 04:50:18.528840: step: 3074/1536, loss: 0.13559147715568542
==================================================
Loss: 0.063
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6541810890184961, 'r': 0.6781789279406714, 'f1': 0.6659638896132097}, 'combined': 0.4380112965563517, 'epoch': 30}
Test Chinese: {'event': {'p': 0.6477166821994408, 'r': 0.7993099482461185, 'f1': 0.7155727155727155}, 'argument': {'p': 0.6772072897688941, 'r': 0.6927034948284965, 'f1': 0.684867747039621}, 'combined': 0.4900726735573092, 'epoch': 30}
Dev Korean: {'event': {'p': 0.554679802955665, 'r': 0.7496671105193076, 'f1': 0.637599093997735}, 'argument': {'p': 0.6579510821035037, 'r': 0.6617708271374864, 'f1': 0.6598554267695053}, 'combined': 0.4207232222777253, 'epoch': 30}
Test Korean: {'event': {'p': 0.5975286849073257, 'r': 0.7786083956296722, 'f1': 0.6761548064918851}, 'argument': {'p': 0.6759565918416632, 'r': 0.6968293607108833, 'f1': 0.6862342944076768}, 'combined': 0.46400061654331803, 'epoch': 30}
Dev Russian: {'event': {'p': 0.6124197002141327, 'r': 0.7616511318242344, 'f1': 0.6789317507418398}, 'argument': {'p': 0.6539402484794373, 'r': 0.6759939518739051, 'f1': 0.6647842468320162}, 'combined': 0.4513431325672561, 'epoch': 30}
Test Russian: {'event': {'p': 0.6584421867440735, 'r': 0.7826336975273146, 'f1': 0.7151865475564897}, 'argument': {'p': 0.6753344402291788, 'r': 0.6891358714635125, 'f1': 0.6821653560484007}, 'combined': 0.4878754858548992, 'epoch': 30}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6778754291685326, 'r': 0.649864047797932, 'f1': 0.6635742597767913}, 'combined': 0.4773915537962527, 'epoch': 30}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.6460727969348659, 'r': 0.47433192686357245, 'f1': 0.5470397404703974}, 'combined': 0.32822384428223844, 'epoch': 30}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5366956110084307, 'r': 0.5366956110084307, 'f1': 0.5366956110084307}, 'combined': 0.2811262724329875, 'epoch': 30}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5556650246305419, 'r': 0.7509986684420772, 'f1': 0.638731596828992}, 'argument': {'p': 0.6587320085968769, 'r': 0.6668350796143493, 'f1': 0.6627587774376343}, 'combined': 0.42332497222517074, 'epoch': 29}
Test for Korean: {'event': {'p': 0.6019417475728155, 'r': 0.7843588269120184, 'f1': 0.6811485642946317}, 'argument': {'p': 0.67064108513621, 'r': 0.7038772013717453, 'f1': 0.6868573154160853}, 'combined': 0.4678518742709315, 'epoch': 29}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6032356532356533, 'r': 0.4963331324090818, 'f1': 0.5445877425044091}, 'combined': 0.34350919142585806, 'epoch': 29}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 31
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 04:56:13.118107: step: 2/1536, loss: -1.4586401448468678e-05
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==================================================
Loss: 0.071
--------------------
Dev Chinese: {'event': {'p': 0.5827263267429761, 'r': 0.7456724367509987, 'f1': 0.6542056074766356}, 'argument': {'p': 0.6700535094053426, 'r': 0.6764443352470663, 'f1': 0.6732337560728379}, 'combined': 0.440433298365408, 'epoch': 31}
Test Chinese: {'event': {'p': 0.6472506989748369, 'r': 0.7987349051178838, 'f1': 0.7150579150579152}, 'argument': {'p': 0.6845109516194635, 'r': 0.6891833813233507, 'f1': 0.6868392201623869}, 'combined': 0.4911298207493207, 'epoch': 31}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.6711419201835144, 'r': 0.6745861968999987, 'f1': 0.6728596508750262}, 'combined': 0.45602713430224323, 'epoch': 31}
Test Russian: {'event': {'p': 0.6589259796806967, 'r': 0.7832087406555491, 'f1': 0.7157120336311087}, 'argument': {'p': 0.6800977425133284, 'r': 0.6897629097379546, 'f1': 0.6848962294248836}, 'combined': 0.4901884731879618, 'epoch': 31}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6612809511039598, 'r': 0.6175598964855162, 'f1': 0.6386730553397219}, 'combined': 0.4594770182300157, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6022574039344247, 'r': 0.5694070000834561, 'f1': 0.5853716823287867}, 'combined': 0.3066232621722216, 'epoch': 31}
New best korean model...
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 32
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 05:47:44.454005: step: 3074/1536, loss: 1.5544284906354733e-05
==================================================
Loss: 0.056
--------------------
Dev Chinese: {'event': {'p': 0.5858480749219563, 'r': 0.7496671105193076, 'f1': 0.6577102803738317}, 'argument': {'p': 0.6531989808123068, 'r': 0.6857870444184967, 'f1': 0.6690964506388053}, 'combined': 0.4400716141467843, 'epoch': 32}
Test Chinese: {'event': {'p': 0.646784715750233, 'r': 0.7981598619896493, 'f1': 0.7145431145431146}, 'argument': {'p': 0.6714551497554235, 'r': 0.6987165495578443, 'f1': 0.6848146486387032}, 'combined': 0.4893295919230477, 'epoch': 32}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6555703328352358, 'r': 0.671235663018844, 'f1': 0.6633105192336483}, 'combined': 0.4259309902666802, 'epoch': 32}
Test Korean: {'event': {'p': 0.6015004413062666, 'r': 0.7837837837837838, 'f1': 0.6806491885143571}, 'argument': {'p': 0.667047339128869, 'r': 0.6978202150181249, 'f1': 0.6820868679951698}, 'combined': 0.46426187319721174, 'epoch': 32}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.6589019790489338, 'r': 0.6859338551124797, 'f1': 0.6721462399343395}, 'combined': 0.45554362374184904, 'epoch': 32}
Test Russian: {'event': {'p': 0.6560232220609579, 'r': 0.7797584818861415, 'f1': 0.7125591171833946}, 'argument': {'p': 0.6664572625755729, 'r': 0.6948416958500866, 'f1': 0.6803535574216139}, 'combined': 0.48479213024892714, 'epoch': 32}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.6432150967034687, 'r': 0.6857417146673344, 'f1': 0.6637979797979796}, 'combined': 0.47755250345178385, 'epoch': 32}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5802068302068303, 'r': 0.48472975688165565, 'f1': 0.5281882868089764}, 'combined': 0.32503894572860087, 'epoch': 32}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5793641958767867, 'r': 0.5898980903472737, 'f1': 0.584583693136938}, 'combined': 0.30621050592887233, 'epoch': 32}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 33
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 05:53:41.987088: step: 2/1536, loss: 0.15876711905002594
2023-01-22 05:53:42.854983: step: 4/1536, loss: 4.731572335003875e-05
2023-01-22 05:53:43.696396: step: 6/1536, loss: 0.0011239787563681602
2023-01-22 05:53:44.625476: step: 8/1536, loss: 0.0007515190518461168
2023-01-22 05:53:45.537593: step: 10/1536, loss: 0.00020168692572042346
2023-01-22 05:53:46.434583: step: 12/1536, loss: 0.0037043916527181864
2023-01-22 05:53:47.329275: step: 14/1536, loss: 0.01122981309890747
2023-01-22 05:53:48.190686: step: 16/1536, loss: 0.0005201878375373781
2023-01-22 05:53:49.072803: step: 18/1536, loss: 0.001276335446164012
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2023-01-22 05:53:56.061906: step: 34/1536, loss: -3.7512381823034957e-06
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2023-01-22 05:54:00.503000: step: 44/1536, loss: 0.01276304293423891
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==================================================
Loss: 0.047
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.66981879317385, 'r': 0.6757159505605603, 'f1': 0.672754448951091}, 'combined': 0.445621229620641, 'epoch': 33}
Test Chinese: {'event': {'p': 0.6481826654240447, 'r': 0.7998849913743531, 'f1': 0.716087516087516}, 'argument': {'p': 0.6803558524740392, 'r': 0.6887151735283278, 'f1': 0.6845099926858889}, 'combined': 0.4901690603995219, 'epoch': 33}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6721807572726208, 'r': 0.6590389177024827, 'f1': 0.6655449691752311}, 'combined': 0.42811952717047713, 'epoch': 33}
Test Korean: {'event': {'p': 0.6054721977052074, 'r': 0.7889591719378953, 'f1': 0.685143570536829}, 'argument': {'p': 0.6760280986665268, 'r': 0.6909387964654152, 'f1': 0.6834021255860308}, 'combined': 0.4682285724364715, 'epoch': 33}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6738142027924054, 'r': 0.6752961988396028, 'f1': 0.6745543868306249}, 'combined': 0.45957770687332783, 'epoch': 33}
Test Russian: {'event': {'p': 0.6608611514271892, 'r': 0.7855089131684876, 'f1': 0.7178139779295848}, 'argument': {'p': 0.6751820028979729, 'r': 0.6852453741716689, 'f1': 0.6801764680836968}, 'combined': 0.4882401762492537, 'epoch': 33}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7117834186016004, 'r': 0.647075835092364, 'f1': 0.6778889700967623}, 'combined': 0.4876899065444333, 'epoch': 33}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6425834701696771, 'r': 0.47781847781847786, 'f1': 0.5480859010270777}, 'combined': 0.33728363140127854, 'epoch': 33}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.576633375185396, 'r': 0.5451806456298289, 'f1': 0.5604660842923475}, 'combined': 0.29357747272456297, 'epoch': 33}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 34
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 06:45:02.728603: step: 3074/1536, loss: 0.00022179115330800414
==================================================
Loss: 0.051
--------------------
Dev Chinese: {'event': {'p': 0.5837669094693028, 'r': 0.7470039946737683, 'f1': 0.655373831775701}, 'argument': {'p': 0.6849090005578441, 'r': 0.6753614796403099, 'f1': 0.680101733838007}, 'combined': 0.44572087930271254, 'epoch': 34}
Test Chinese: {'event': {'p': 0.6481826654240447, 'r': 0.7998849913743531, 'f1': 0.716087516087516}, 'argument': {'p': 0.6878576681133601, 'r': 0.6756541202655626, 'f1': 0.6817012827877329}, 'combined': 0.48815777830514095, 'epoch': 34}
Dev Korean: {'event': {'p': 0.5586206896551724, 'r': 0.7549933422103862, 'f1': 0.6421291053227633}, 'argument': {'p': 0.6893268575565384, 'r': 0.6583795257907851, 'f1': 0.673497870457092}, 'combined': 0.43247258499339886, 'epoch': 34}
Test Korean: {'event': {'p': 0.6045895851721095, 'r': 0.7878090856814262, 'f1': 0.6841448189762797}, 'argument': {'p': 0.6886157639052056, 'r': 0.6745910029295396, 'f1': 0.6815312395851932}, 'combined': 0.4662660665326915, 'epoch': 34}
Dev Russian: {'event': {'p': 0.6102783725910065, 'r': 0.758988015978695, 'f1': 0.6765578635014837}, 'argument': {'p': 0.6848063972522143, 'r': 0.673242118884519, 'f1': 0.6789750210445871}, 'combined': 0.45936588960880076, 'epoch': 34}
Test Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6856514316095479, 'r': 0.6754284733712641, 'f1': 0.6805015605948387}, 'combined': 0.48632796763477687, 'epoch': 34}
Chinese: {'event': {'p': 0.611764705882353, 'r': 0.9629629629629629, 'f1': 0.748201438848921}, 'argument': {'p': 0.6469737699576408, 'r': 0.6630144419400616, 'f1': 0.6548958977530405}, 'combined': 0.4899940529950807, 'epoch': 34}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.643095238095238, 'r': 0.4122405372405372, 'f1': 0.5024181547619047}, 'combined': 0.30145089285714277, 'epoch': 34}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5628295371423568, 'r': 0.5628295371423568, 'f1': 0.5628295371423568}, 'combined': 0.29481547183647266, 'epoch': 34}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 35
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 07:13:36.942709: step: 3068/1536, loss: 0.00113310304004699
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2023-01-22 07:13:38.785770: step: 3072/1536, loss: 0.0007560342783108354
2023-01-22 07:13:39.625626: step: 3074/1536, loss: 0.0015845497837290168
==================================================
Loss: 0.055
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6879000605988896, 'r': 0.6808395027921561, 'f1': 0.6843515709716294}, 'combined': 0.4517040158866479, 'epoch': 35}
Test Chinese: {'event': {'p': 0.6495806150978565, 'r': 0.8016101207590569, 'f1': 0.7176319176319177}, 'argument': {'p': 0.6848552929065814, 'r': 0.6888288560394864, 'f1': 0.686836327444511}, 'combined': 0.4928956707632681, 'epoch': 35}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6873400466665833, 'r': 0.6614590311512595, 'f1': 0.6741512339630179}, 'combined': 0.4336556068980682, 'epoch': 35}
Test Korean: {'event': {'p': 0.5988526037069727, 'r': 0.780333525014376, 'f1': 0.6776529338327091}, 'argument': {'p': 0.6840808600615222, 'r': 0.6889588560076315, 'f1': 0.6865111930104805}, 'combined': 0.4652163240525453, 'epoch': 35}
Dev Russian: {'event': {'p': 0.6113490364025695, 'r': 0.7603195739014648, 'f1': 0.6777448071216616}, 'argument': {'p': 0.6876855884155878, 'r': 0.68264390521606, 'f1': 0.6851554721962515}, 'combined': 0.4643605633519995, 'epoch': 35}
Test Russian: {'event': {'p': 0.6569908079342042, 'r': 0.7809085681426107, 'f1': 0.7136100893326327}, 'argument': {'p': 0.6797230678527303, 'r': 0.6839669163512906, 'f1': 0.6818383886033094}, 'combined': 0.4865667534016259, 'epoch': 35}
Chinese: {'event': {'p': 0.5647058823529412, 'r': 0.8888888888888888, 'f1': 0.6906474820143885}, 'argument': {'p': 0.7269282269282269, 'r': 0.6668515139589519, 'f1': 0.695595113698562}, 'combined': 0.4804110137774241, 'epoch': 35}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.611574074074074, 'r': 0.4644866385372714, 'f1': 0.5279776179056753}, 'combined': 0.31678657074340516, 'epoch': 35}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6008139240426689, 'r': 0.5680422554585234, 'f1': 0.5839686738358651}, 'combined': 0.3058883529616437, 'epoch': 35}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 36
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 07:19:35.823974: step: 2/1536, loss: 0.00030550468363799155
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2023-01-22 07:19:37.600315: step: 6/1536, loss: 0.044370025396347046
2023-01-22 07:19:38.495757: step: 8/1536, loss: 0.000681348901707679
2023-01-22 07:19:39.385520: step: 10/1536, loss: 0.009220109321177006
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==================================================
Loss: 0.053
--------------------
Dev Chinese: {'event': {'p': 0.5900104058272633, 'r': 0.7549933422103862, 'f1': 0.6623831775700936}, 'argument': {'p': 0.6840594351758154, 'r': 0.6629960214827184, 'f1': 0.6733630478796931}, 'combined': 0.4460243553128342, 'epoch': 36}
Test Chinese: {'event': {'p': 0.6486486486486487, 'r': 0.8004600345025877, 'f1': 0.7166023166023167}, 'argument': {'p': 0.6863425586245447, 'r': 0.6718192689881209, 'f1': 0.6790032625509467}, 'combined': 0.48657531092453943, 'epoch': 36}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6884772555730765, 'r': 0.649591501818768, 'f1': 0.6684693472516532}, 'combined': 0.43000066731476677, 'epoch': 36}
Test Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6862740308084058, 'r': 0.6753216099432122, 'f1': 0.6807537707625398}, 'combined': 0.46403440553851033, 'epoch': 36}
Dev Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6871587624073167, 'r': 0.6604583119618711, 'f1': 0.67354402804934}, 'combined': 0.4596888025262557, 'epoch': 36}
Test Russian: {'event': {'p': 0.6579583938074504, 'r': 0.78205865439908, 'f1': 0.7146610614818706}, 'argument': {'p': 0.6810826014944508, 'r': 0.6709312836905474, 'f1': 0.6759688330529874}, 'combined': 0.4830886037583093, 'epoch': 36}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7024752024752023, 'r': 0.6444194006177476, 'f1': 0.6721960989202367}, 'combined': 0.48359431576995443, 'epoch': 36}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.6436980210565116, 'r': 0.4318480394429762, 'f1': 0.5169090169090169}, 'combined': 0.318097856559395, 'epoch': 36}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5409003508433117, 'r': 0.48189303984222315, 'f1': 0.5096945613715821}, 'combined': 0.26698286548035255, 'epoch': 36}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 37
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 08:10:58.539577: step: 3074/1536, loss: 0.10678264498710632
==================================================
Loss: 0.050
--------------------
Dev Chinese: {'event': {'p': 0.5910509885535901, 'r': 0.7563249001331558, 'f1': 0.6635514018691588}, 'argument': {'p': 0.6644197293514574, 'r': 0.6824560682993693, 'f1': 0.673317134340963}, 'combined': 0.44678052839447074, 'epoch': 37}
Test Chinese: {'event': {'p': 0.6495806150978565, 'r': 0.8016101207590569, 'f1': 0.7176319176319177}, 'argument': {'p': 0.6772882115130735, 'r': 0.6904686239597261, 'f1': 0.6838149110999282}, 'combined': 0.4907274059579408, 'epoch': 37}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6636237463603191, 'r': 0.6631428595875942, 'f1': 0.6633832158252121}, 'combined': 0.42672895423411156, 'epoch': 37}
Test Korean: {'event': {'p': 0.6010591350397175, 'r': 0.7832087406555491, 'f1': 0.6801498127340824}, 'argument': {'p': 0.6770573302806168, 'r': 0.6910812681913528, 'f1': 0.683997423903053}, 'combined': 0.46522071977825624, 'epoch': 37}
Dev Russian: {'event': {'p': 0.6156316916488223, 'r': 0.7656458055925432, 'f1': 0.6824925816023738}, 'argument': {'p': 0.6659931833282474, 'r': 0.6777201359327066, 'f1': 0.6718054874736575}, 'combined': 0.45850226148053774, 'epoch': 37}
Test Russian: {'event': {'p': 0.660377358490566, 'r': 0.7849338700402531, 'f1': 0.717288491854966}, 'argument': {'p': 0.6726681543267404, 'r': 0.6854964305014876, 'f1': 0.6790217089356998}, 'combined': 0.4870544575392698, 'epoch': 37}
Chinese: {'event': {'p': 0.5647058823529412, 'r': 0.8888888888888888, 'f1': 0.6906474820143885}, 'argument': {'p': 0.7070893665721252, 'r': 0.6778707977055085, 'f1': 0.6921718693870592}, 'combined': 0.47804675871336466, 'epoch': 37}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5818211702827087, 'r': 0.47871362111868443, 'f1': 0.5252552231718899}, 'combined': 0.32323398349039373, 'epoch': 37}
Russian: {'event': {'p': 0.4375, 'r': 0.5833333333333334, 'f1': 0.5}, 'argument': {'p': 0.5592390179391087, 'r': 0.5799515741590757, 'f1': 0.5694070000834561}, 'combined': 0.28470350004172806, 'epoch': 37}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 38
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
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2023-01-22 08:39:37.437318: step: 3074/1536, loss: 4.2678966565290466e-05
==================================================
Loss: 0.048
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6926827726455232, 'r': 0.6647520156840102, 'f1': 0.6784300406980845}, 'combined': 0.44779552919908616, 'epoch': 38}
Test Chinese: {'event': {'p': 0.6500465983224604, 'r': 0.8021851638872916, 'f1': 0.718146718146718}, 'argument': {'p': 0.6988417312960284, 'r': 0.6730735827880151, 'f1': 0.685715660516903}, 'combined': 0.49244445118202296, 'epoch': 38}
Dev Korean: {'event': {'p': 0.5605911330049261, 'r': 0.7576564580559254, 'f1': 0.6443941109852774}, 'argument': {'p': 0.693854295535545, 'r': 0.6476642845447562, 'f1': 0.6699641027251894}, 'combined': 0.4317209223676475, 'epoch': 38}
Test Korean: {'event': {'p': 0.6019417475728155, 'r': 0.7843588269120184, 'f1': 0.6811485642946317}, 'argument': {'p': 0.6983235805995694, 'r': 0.6741371611696353, 'f1': 0.6860172562697222}, 'combined': 0.4672796691894637, 'epoch': 38}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6924351929978138, 'r': 0.6599693670990152, 'f1': 0.6758125927157959}, 'combined': 0.46043492963663724, 'epoch': 38}
Test Russian: {'event': {'p': 0.6623125302370585, 'r': 0.7872340425531915, 'f1': 0.7193904361534419}, 'argument': {'p': 0.6943887689627302, 'r': 0.6666902922264437, 'f1': 0.6802576933246637}, 'combined': 0.4893708786975641, 'epoch': 38}
Chinese: {'event': {'p': 0.611764705882353, 'r': 0.9629629629629629, 'f1': 0.748201438848921}, 'argument': {'p': 0.6912487407770427, 'r': 0.6055567481187316, 'f1': 0.6455715112102778}, 'combined': 0.4830175335674022, 'epoch': 38}
Korean: {'event': {'p': 0.5970149253731343, 'r': 0.6349206349206349, 'f1': 0.6153846153846153}, 'argument': {'p': 0.5833333333333334, 'r': 0.4873417721518987, 'f1': 0.5310344827586206}, 'combined': 0.3267904509283819, 'epoch': 38}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.58170112051691, 'r': 0.465360896413528, 'f1': 0.5170676626816978}, 'combined': 0.27084496616660364, 'epoch': 38}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}
******************************
Epoch: 39
command: python train.py --model_name arg --xlmr_model_name xlm-roberta-large --batch_size 16 --xlmr_learning_rate 2e-5 --max_epoch 40 --p1_data_weight 0.4 --event_hidden_num 450 --role_hidden_num 350 --learning_rate 2e-4
2023-01-22 08:45:28.458456: step: 2/1536, loss: -7.463005658792099e-06
2023-01-22 08:45:29.335148: step: 4/1536, loss: -7.395945885946276e-06
2023-01-22 08:45:30.197769: step: 6/1536, loss: 3.980804467573762e-05
2023-01-22 08:45:31.066155: step: 8/1536, loss: 0.11144053936004639
2023-01-22 08:45:31.901839: step: 10/1536, loss: 0.0008972943760454655
2023-01-22 08:45:32.782921: step: 12/1536, loss: 0.001465059700421989
2023-01-22 08:45:33.682343: step: 14/1536, loss: 0.24016867578029633
2023-01-22 08:45:34.610286: step: 16/1536, loss: 2.773795677057933e-05
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2023-01-22 09:08:00.557128: step: 3072/1536, loss: 0.0007764673791825771
2023-01-22 09:08:01.394335: step: 3074/1536, loss: 2.011571814364288e-05
==================================================
Loss: 0.045
--------------------
Dev Chinese: {'event': {'p': 0.5879292403746098, 'r': 0.7523302263648469, 'f1': 0.6600467289719626}, 'argument': {'p': 0.6743644325671124, 'r': 0.666942960455222, 'f1': 0.6706331649579252}, 'combined': 0.44264922687059316, 'epoch': 39}
Test Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6968527847604905, 'r': 0.6811557595747593, 'f1': 0.6889148690210718}, 'combined': 0.496160567186862, 'epoch': 39}
Dev Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.674442998387488, 'r': 0.6500243525371083, 'f1': 0.6620085773257718}, 'combined': 0.42584470206233116, 'epoch': 39}
Test Korean: {'event': {'p': 0.6037069726390115, 'r': 0.7866589994249569, 'f1': 0.6831460674157304}, 'argument': {'p': 0.6946258102124493, 'r': 0.6837796502430763, 'f1': 0.6891600580280886}, 'combined': 0.47079698346188525, 'epoch': 39}
Dev Russian: {'event': {'p': 0.6145610278372591, 'r': 0.7643142476697736, 'f1': 0.6813056379821959}, 'argument': {'p': 0.6771735198128065, 'r': 0.6642655201682809, 'f1': 0.670657416365311}, 'combined': 0.45692267892425936, 'epoch': 39}
Test Russian: {'event': {'p': 0.6594097726173198, 'r': 0.7837837837837838, 'f1': 0.7162375197057278}, 'argument': {'p': 0.6916729709407391, 'r': 0.6775376383167336, 'f1': 0.6845323400946317}, 'combined': 0.49028774542773673, 'epoch': 39}
Chinese: {'event': {'p': 0.5764705882352941, 'r': 0.9074074074074074, 'f1': 0.7050359712230215}, 'argument': {'p': 0.6933287905510128, 'r': 0.6188389204918131, 'f1': 0.653969514231523}, 'combined': 0.4610720316164695, 'epoch': 39}
Korean: {'event': {'p': 0.582089552238806, 'r': 0.6190476190476191, 'f1': 0.6}, 'argument': {'p': 0.5848543219232876, 'r': 0.43489167527629075, 'f1': 0.49884633340515705}, 'combined': 0.2993078000430942, 'epoch': 39}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.5758586025503319, 'r': 0.5130376640902957, 'f1': 0.5426359908647359}, 'combined': 0.28423789997676646, 'epoch': 39}
==================================================
Current best result:
--------------------
Dev for Chinese: {'event': {'p': 0.586888657648283, 'r': 0.7509986684420772, 'f1': 0.6588785046728971}, 'argument': {'p': 0.6915908579146455, 'r': 0.6571127213030649, 'f1': 0.6739110916220906}, 'combined': 0.44402553233044284, 'epoch': 23}
Test for Chinese: {'event': {'p': 0.651910531220876, 'r': 0.80448533640023, 'f1': 0.7202059202059202}, 'argument': {'p': 0.6957089274665749, 'r': 0.6821700374475941, 'f1': 0.6888729666209285}, 'combined': 0.49613038883020794, 'epoch': 23}
Chinese: {'event': {'p': 0.5882352941176471, 'r': 0.9259259259259259, 'f1': 0.7194244604316546}, 'argument': {'p': 0.7727980080921257, 'r': 0.6514495605404697, 'f1': 0.7069542316179087}, 'combined': 0.508600166631589, 'epoch': 23}
--------------------
Dev for Korean: {'event': {'p': 0.5596059113300492, 'r': 0.7563249001331558, 'f1': 0.6432616081540204}, 'argument': {'p': 0.6682124370718079, 'r': 0.6570836274753911, 'f1': 0.662601306713045}, 'combined': 0.4262259821211886, 'epoch': 31}
Test for Korean: {'event': {'p': 0.6023830538393645, 'r': 0.7849338700402531, 'f1': 0.6816479400749064}, 'argument': {'p': 0.6799715298478611, 'r': 0.6945176101128578, 'f1': 0.6871676001284617}, 'combined': 0.46840637911378286, 'epoch': 31}
Korean: {'event': {'p': 0.6119402985074627, 'r': 0.6507936507936508, 'f1': 0.6307692307692307}, 'argument': {'p': 0.6245659722222222, 'r': 0.5059774964838256, 'f1': 0.5590520590520591}, 'combined': 0.3526328372482219, 'epoch': 31}
--------------------
Dev for Russian: {'event': {'p': 0.6177730192719486, 'r': 0.7683089214380826, 'f1': 0.6848664688427301}, 'argument': {'p': 0.6763773081024169, 'r': 0.675385552518689, 'f1': 0.6758810664970594}, 'combined': 0.4628882793694995, 'epoch': 28}
Test for Russian: {'event': {'p': 0.659893565553943, 'r': 0.7843588269120184, 'f1': 0.7167630057803469}, 'argument': {'p': 0.6796115956978651, 'r': 0.6930249824550598, 'f1': 0.6862527513561504}, 'combined': 0.49188058478706737, 'epoch': 28}
Russian: {'event': {'p': 0.4583333333333333, 'r': 0.6111111111111112, 'f1': 0.5238095238095238}, 'argument': {'p': 0.6142575973539878, 'r': 0.6030892774020971, 'f1': 0.6086222065525749}, 'combined': 0.31880210819420596, 'epoch': 28}